Interactive graphical user interface for customizable combinatorial test construction

ABSTRACT

A computing device receives a request for a design of a combinatorial test for a test system. The device receives a run indication of a total quantity of test cases for the design, a factor indication of a total quantity of factors, and/or a strength indication for a covering array. The device generates an updated design by: selecting test case(s) to remove from a first design; or adding test case(s) to the first design. The first design comprises a set of test cases that represent the covering array according to the strength indication. The updated design is constrained to the total quantity of test cases as indicated by the run indication. The device outputs a respective setting for each test condition for at least one test case of the updated design for testing the test system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of, and priority based on, 35 U.S.C.§ 119 to U.S. Provisional Application No. 63/303,498, filed Jan. 26,2022, and is a continuation-in-part of U.S. application Ser. No.17/365,083, filed Jul. 1, 2021, which claims the benefit of, andpriority based on, 35 U.S.C. § 119 to U.S. Provisional Application No.63/172,354, filed Apr. 8, 2021 and is a continuation-in-part of U.S.application Ser. No. 17/074,022, filed Oct. 19, 2020, which issued asU.S. Pat. No. 11,074,483 on Jul. 27, 2021, which claims the benefit of,and priority based on, 35 U.S.C. § 119 to U.S. Provisional ApplicationNo. 63/057,025, filed Jul. 27, 2020, and is a continuation-in-part ofU.S. application Ser. No. 16/663,474, filed Oct. 25, 2019, which issuedas U.S. Pat. No. 10,878,345 on Dec. 29, 2020, and which claims thebenefit of, and priority based on, 35 U.S.C. § 119 to U.S. ProvisionalApplication No. 62/886,162, filed Aug. 13, 2019, and is acontinuation-in-part of U.S. application Ser. No. 16/154,332, filed Oct.8, 2018, which issued as U.S. Pat. No. 10,503,846 on Dec. 10, 2019, andwhich claims the benefit of, and priority based on, 35 U.S.C. § 119 toU.S. Provisional Application No. 62/681,651, filed Jun. 6, 2018, andU.S. Provisional Application No. 62/661,061, filed Apr. 22, 2018, thedisclosures of each of which are incorporated herein by reference intheir entirety.

BACKGROUND

Design engineers may use a design for conducting an experiment (e.g., aguide as to what test conditions should be tested during which tests).Experiments are often conducted on computer models or designed bycomputer models. As an example, hyperparameters are factors used tocontrol the behavior of a computer model (e.g., a computer model of amachine learning algorithm). Experiments can be conducted to determinethe best inputs to the hyperparameters for a response according to thecomputer model. Many hyperparameters are a type of continuous factorsthat can have infinite inputs with a range of candidate inputs for agiven hyperparameter.

In some experiments (e.g., ones involving continuous factors), it isuseful to distribute design points uniformly across a design space toobserve responses in the experiment for different input factors at thatdesign point. Such a design can be considered a space-filling design.For instance, if an experiment tests strain on a physical object like apitcher, flask or drinking bottle, the design space models the shape ofthe physical object, and the design points distributed over the designspace represents test points for strain. The dimensions of the designspace define continuous variables for the design space (e.g., theheight, width, and length of the object). The bottle in the experimentaltest, for example, may be a narrow-necked container made of impermeablematerial of various sizes to hold liquids of various temperatures.

In some experiments, it also helpful to observe how design options forthe design space would influence the experiments. A categorical factorcan be used to describe a design option or level at a design point forthe design space. A categorical factor for the design space of thebottle could be a material type, with each of the design points takingon one of a set of levels that represent material types in theexperiment. The material types for the bottle, for example, may beglass, metal, ceramic, and/or various types of plastic. In those caseswhere categorical factors are also employed, particularly on anon-rectangular design space like a bottle, it can be difficult todistribute the levels uniformly across the design.

SUMMARY

In an example embodiment, a computer-program product tangibly embodiedin a non-transitory machine-readable storage medium. Thecomputer-program product includes instructions operable to cause acomputing system to receive, using a graphical user interface, a requestfor a design of a combinatorial test for a test system. The designcomprises a plurality of test cases for testing the test system. Eachtest case of the plurality of test cases comprises multiple testconditions for testing one of factors in the test system. A testcondition of the multiple test conditions comprises one of options for agiven factor in the test system. The computer-program product includesinstructions operable to cause a computing system to receive, using thegraphical user interface, a run indication of a total quantity of theplurality of test cases for the design. The computer-program productincludes instructions operable to cause a computing system to receive,using the graphical user interface, a factor indication of a totalquantity of the factors. The computer-program product includesinstructions operable to cause a computing system to receive, using thegraphical user interface, a strength indication for a covering array,wherein the covering array comprises all combinations for any subset oft factors, and wherein t is a numerical value. The computer-programproduct includes instructions operable to cause a computing system togenerate an updated design by: selecting, by the computing system, ofone or more test cases to remove from a first design; or adding, by thecomputing system, one or more computer-generated test cases to the firstdesign. The first design comprises a set of test cases that representthe covering array according to the strength indication. The set of testcases comprise more or fewer test cases than the total quantity of theplurality of test cases. The updated design is constrained to the totalquantity of the plurality of test cases as indicated by the runindication. The computer-program product includes instructions operableto cause a computing system to, responsive to receiving the request forthe design of the combinatorial test for the test system, output arespective setting for each test condition for at least one test case ofthe updated design for testing the test system.

In another example embodiment, a computing device is provided. Thecomputing device includes, but is not limited to, a processor andmemory. The memory contains instructions that when executed by theprocessor control the computing device to output a respective settingfor each test condition for at least one test case of the updated designfor testing the test system.

In another example embodiment, a method is provided of outputting arespective setting for each test condition for at least one test case ofthe updated design for testing the test system.

Other features and aspects of example embodiments are presented below inthe Detailed Description when read in connection with the drawingspresented with this application.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to at least oneembodiment of the present technology.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to at least one embodiment of the present technology.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to at least one embodiment ofthe present technology.

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to at least oneembodiment of the present technology.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to at least one embodiment of the presenttechnology.

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to at least oneembodiment of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to at least oneembodiment of the present technology.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to at least one embodiment ofthe present technology.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according toat least one embodiment of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to at least oneembodiment of the present technology.

FIG. 11 illustrates a flow chart of an example of a process forgenerating and using a machine-learning model according to at least oneembodiment of the present technology.

FIG. 12 illustrates an example of a machine-learning model as a neuralnetwork.

FIG. 13 illustrates an example block diagram of a system for outputtinga design for a design space in at least one embodiment of the presenttechnology.

FIGS. 14A-14B illustrate an example flow diagram for a design space inat least one embodiment of the present technology.

FIG. 15 illustrates an example flow diagram in at least one embodimentinvolving level swapping.

FIGS. 16A-16J illustrate an example block diagram of design pointselection in at least one embodiment involving level swapping.

FIG. 17 illustrates an example flow diagram in at least one embodimentinvolving design point replacement.

FIGS. 18A-18F illustrate an example block diagram of design pointselection in at least one embodiment involving design point replacement.

FIGS. 19A-19B illustrate an example block diagram of design pointselection in at least one embodiment involving level swapping and designpoint replacement.

FIGS. 20A-20B illustrate an example block diagram of design pointselection in at least one embodiment involving level swapping and designpoint replacement.

FIG. 21 illustrates example clustering in at least one embodiment of thepresent technology.

FIG. 22 illustrates an example of design spaces in some embodiments ofthe present technology.

FIGS. 23A-23B illustrate examples of graphical interfaces in at leastone embodiment of the present technology.

FIGS. 24A-24F illustrate an example block diagram of design pointselection for a non-rectangular design space in at least one embodimentof the present technology.

FIGS. 25A-25B illustrate examples of a design space in at least oneembodiment of the present technology.

FIGS. 26A-26D illustrate examples of sub-designs in at least oneembodiment of the present technology.

FIG. 27 illustrates an example of a design space in at least oneembodiment of the present technology.

FIGS. 28A-28B illustrate examples of sub-designs in at least oneembodiment of the present technology.

FIG. 29 illustrates an example of a comparison of performance in atleast one embodiment of the present technology compared to slicedhypercube design.

FIG. 30 illustrates an example of a comparison of performance ofdifferent weights for a sub-design in at least one embodiment involvingdesign point replacement.

FIG. 31 illustrates an example of a comparison of performance ofdifferent weights for a design in at least one embodiment involvingdesign point replacement.

FIG. 32 illustrates an example block diagram of a system for outputtinga selected design case in at least one embodiment of the presenttechnology.

FIG. 33 illustrates an example flow diagram for outputting a selecteddesign case in at least one embodiment of the present technology.

FIGS. 34A-B illustrate an example graphical user interface forcontrolling generation of a design suite in at least one embodiment ofthe present technology.

FIG. 34C illustrates an example graphical user interface for disallowingcombinations in at least one embodiment of the present technology.

FIG. 35 illustrates an example of a design suite in at least oneembodiment of the present technology.

FIG. 36 illustrates an example of a graphical user interface forcontrolling an indication of a selected design case in at least oneembodiment of the present technology.

FIG. 37 illustrates an example of model results for individual factorsin at least one embodiment of the present technology.

FIGS. 38A-B illustrates an example of model results for design suites inat least one embodiment of the present technology.

FIG. 39 illustrates an example of a graphical user interface forcontrolling generation of a design suite in at least one embodiment ofthe present technology.

FIG. 40 illustrates an example block diagram of a validation system forlocating deviation from a specified result of a validation specificationin at least one embodiment of the present technology.

FIG. 41A illustrates an example flow diagram of a method for receiving arequest to validate according to a validation specification in at leastone embodiment of the present technology.

FIG. 41B illustrates an example flow diagram of a method for locatingdeviation from a specified result of a validation specification in atleast one embodiment of the present technology.

FIG. 42 illustrates an example graphical user interface for validating asystem of operation according to a validation specification in at leastone embodiment of the present technology.

FIGS. 43A-E illustrate generating an example test suite for validating asystem of operation according to a validation specification in at leastone embodiment of the present technology.

FIG. 44 illustrates an example graphical user interface for displaying aresponse of validating a system of operation according to a validationspecification in at least one embodiment of the present technology.

FIG. 45 illustrates an example graphical user interface for displayingpotential causes in response to a single test case failure in at leastone embodiment of the present technology.

FIGS. 46A-B illustrate example graphical user interfaces for displayingpotential causes in response to multiple test case failures in at leastone embodiment of the present technology.

FIGS. 47A-B illustrate example graphical user interfaces for displayingpotential causes ranked based on weights and commonalities in at leastone embodiment of the present technology.

FIGS. 48A-B illustrate example graphical user interfaces for editing atest suite in at least one embodiment of the present technology.

FIGS. 49A-C illustrate example graphical user interfaces for editingdisallowed combinations in at least one embodiment of the presenttechnology.

FIGS. 50A-B illustrate example graphical user interfaces for editingvalidation specifications in at least one embodiment of the presenttechnology.

FIG. 51 illustrates example test suites in at least one embodiment ofthe present technology.

FIG. 52A illustrates example test suite metrics at least one embodimentof the present technology.

FIG. 52B illustrates example editing of factor inputs in at least oneembodiment of the present technology.

FIG. 52C illustrates example test suite metrics at least one embodimentof the present technology.

FIG. 53 illustrates an example block diagram of a system for generatingdata for a design suite in at least one embodiment of the presenttechnology.

FIG. 54 illustrates an example flow diagram of a method for generatingdata for a design suite in at least one embodiment of the presenttechnology.

FIG. 55 illustrates an example graphical user interface forcomputer-generated data for an example software platform in at least oneembodiment of the present technology.

FIGS. 56A-56C illustrate example graphical user interfaces for datageneration for inputs and outputs for an example software platform in atleast one embodiment of the present technology.

FIGS. 57A-57B illustrate example graphical user interfaces forplatform-specific data generation for an example software platform in atleast one embodiment of the present technology.

FIGS. 58A-58B illustrate an example of a graphical user interface forsetting disallowed combinations for a design space of a design suite inat least one embodiment of the present technology.

FIG. 59 illustrates an example of a graphical user interface forspecifying characteristics for design spaces of a design suite in atleast one embodiment of the present technology.

FIG. 60 illustrates an example graphical user interface for specifyingexecution of the design suite in at least one embodiment of the presenttechnology.

FIGS. 61A-61C illustrate an example graphical user interface of a visualsummary of a design suite in at least one embodiment of the presenttechnology.

FIGS. 62A-E illustrate example graphical user interfaces for designcases corresponding to a single design space of multiple differentdesign spaces of a design suite in at least one embodiment of thepresent technology.

FIG. 63 illustrates an example block diagram for outputting settings fora system under test in at least one embodiment of the presenttechnology.

FIG. 64 illustrates an example method for outputting settings for asystem under test in at least one embodiment of the present technology.

FIGS. 65A-65B illustrate example graphical user interfaces for usercontrol of design information in at least one embodiment of the presenttechnology.

FIGS. 66A-66C illustrate example graphical user interfaces forspecifying quantity of runs and coverage criteria in at least oneembodiment of the present technology.

FIGS. 67A-67B illustrate example graphical user interfaces forspecifying weights in at least one embodiment of the present technology.

FIGS. 68A-68D illustrate example graphical user interfaces for visualrepresentations of coverage performance in at least one embodiment ofthe present technology.

FIGS. 69A-69B illustrate example flow diagrams for generating an updateddesign in at least one embodiment of the present technology.

FIGS. 70A-70C illustrate an example of a design fractal plot for visualrepresentations of test case coverage in at least one embodiment of thepresent technology.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the technology. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive.

The ensuing description provides example embodiments only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the example embodimentswill provide those skilled in the art with an enabling description forimplementing an example embodiment. It should be understood that variouschanges may be made in the function and arrangement of elements withoutdeparting from the spirit and scope of the technology as set forth inthe appended claims.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known circuits,processes, algorithms, structures, and techniques may be shown withoutunnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed, but could have additional operationsnot included in a figure. A process may correspond to a method, afunction, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination can correspond to a return ofthe function to the calling function or the main function.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1 , computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10 ), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1 . Services provided by thecloud network can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, and/orsystems. In some embodiments, the computers, servers, and/or systemsthat make up the cloud network 116 are different from the user's ownon-premises computers, servers, and/or systems. For example, the cloudnetwork 116 may host an application, and a user may, via a communicationnetwork such as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or a remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system,between servers 106 and computing environment 114 or between a serverand a device) may occur over one or more networks 108. Networks 108 mayinclude one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired and/or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108, as will be further described with respect toFIG. 2 . The one or more networks 108 can be incorporated entirelywithin or can include an intranet, an extranet, or a combinationthereof. In one embodiment, communications between two or more systemsand/or devices can be achieved by a secure communications protocol, suchas secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. IoT may be implemented in various areas, such asfor access (technologies that get data and move it), embed-ability(devices with embedded sensors), and services. Industries in the IoTspace may include automotive (connected car), manufacturing (connectedfactory), smart cities, energy and retail. This will be describedfurther below with respect to FIG. 2 .

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2 , network device 204 can transmit a communicationover a network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, electrical current, among others.The sensors may be mounted to various components used as part of avariety of different types of systems (e.g., an oil drilling operation).The network devices may detect and record data related to theenvironment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values calculated fromthe data and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2 , computing environment 214 may includea web server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 320(or computing environment 214 in FIG. 2 ) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 302-314. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bytes of data and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 302. Physical layer 302represents physical communication and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 302 also defines protocols that may controlcommunications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 322 and 324 are shown to operate inlower levels, such as physical layer 302 and link layer 304,respectively. For example, a hub can operate in the physical layer and aswitch can operate in the link layer. Inter-network connectioncomponents 326 and 328 are shown to operate on higher levels, such aslayers 306-314. For example, routers can operate in the network layerand network devices can operate in the transport, session, presentation,and application layers.

As noted, a computing environment 320 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 320 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 320 may control which devices it will receive data from. Forexample, if the computing environment 320 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 320 may instruct the hub toprevent any data from being transmitted to the computing environment 320from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 320can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 320 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 320 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3 . For example, referringback to FIG. 2 , one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a Hadoop data node).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may sent periodically, at fixed time intervals, betweenknown fixed stages of the project's execution, among other protocols.The communications transmitted by primary control node 402 may be ofvaried types and may include a variety of types of information. Forexample, primary control node 402 may transmit snapshots (e.g., statusinformation) of the communications grid so that backup control node 404always has a recent snapshot of the communications grid. The snapshot orgrid status may include, for example, the structure of the grid(including, for example, the worker nodes in the grid, uniqueidentifiers of the nodes, or their relationships with the primarycontrol node) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes in thecommunications grid. The backup control nodes may receive and store thebackup data received from the primary control node. The backup controlnodes may transmit a request for such a snapshot (or other information)from the primary control node, or the primary control node may send suchinformation periodically to the backup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart 500 showing an example process foradjusting a communications grid or a work project in a communicationsgrid after a failure of a node, according to embodiments of the presenttechnology. The process may include, for example, receiving grid statusinformation including a project status of a portion of a project beingexecuted by a node in the communications grid, as described in operation502. For example, a control node (e.g., a backup control node connectedto a primary control node and a worker node on a communications grid)may receive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4 , communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 include multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG. 2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DBMS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1 . Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DBMS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a nodes 602 or 610. The database mayorganize data stored in data stores 624. The DBMS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4 , data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart 700 showing an example method forexecuting a project within a grid computing system, according toembodiments of the present technology. As described with respect to FIG.6 , the GESC at the control node may transmit data with a client device(e.g., client device 630) to receive queries for executing a project andto respond to those queries after large amounts of data have beenprocessed. The query may be transmitted to the control node, where thequery may include a request for executing a project, as described inoperation 702. The query can contain instructions on the type of dataanalysis to be performed in the project and whether the project shouldbe executed using the grid-based computing environment, as shown inoperation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project inoperation 712.

As noted with respect to FIG. 2 , the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2 , and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10 , may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2 ) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2 .

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2 . As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2 .The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 1000 interfacing between publishingdevice 1022 and event subscribing devices 1024 a-c, according toembodiments of the present technology. ESP system 1000 may include ESPdevice or subsystem 1001, event publishing device 1022, an eventsubscribing device A 1024 a, an event subscribing device B 1024 b, andan event subscribing device C 1024 c. Input event streams are output toESP device 1001 by publishing device 1022. In alternative embodiments,the input event streams may be created by a plurality of publishingdevices. The plurality of publishing devices further may publish eventstreams to other ESP devices. The one or more continuous queriesinstantiated by ESPE 800 may analyze and process the input event streamsto form output event streams output to event subscribing device A 1024a, event subscribing device B 1024 b, and event subscribing device C1024 c. ESP system 1000 may include a greater or a fewer number of eventsubscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9 , operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 1022.

ESP subsystem 1001 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device1022. The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2 , data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11 .

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12 . The neural network 1200 is representedas multiple layers of interconnected neurons, such as neuron 1208, thatcan exchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 and adesired output of the neural network 1200. Based on the gradient, one ormore numeric weights of the neural network 1200 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neuralnetwork 1200. For example, this process can be repeated hundreds orthousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:y=max(x,0)where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1204, of the neural network 1200. The subsequent layerof the neural network 1200 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1200. This process continues until the neural network 1200outputs a final result at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedilyexecuted and processed with machine-learning specific processors (e.g.,not a generic CPU). Such processors may also provide an energy savingswhen compared to generic CPUs. For example, some of these processors caninclude a graphical processing unit (GPU), an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), anartificial intelligence (AI) accelerator, a neural computing core, aneural computing engine, a neural processing unit, a purpose-built chiparchitecture for deep learning, and/or some other machine-learningspecific processor that implements a machine learning approach or one ormore neural networks using semiconductor (e.g., silicon (Si), galliumarsenide (GaAs)) devices. Furthermore, these processors may also beemployed in heterogeneous computing architectures with a number of and avariety of different types of cores, engines, nodes, and/or layers toachieve various energy efficiencies, processing speed improvements, datacommunication speed improvements, and/or data efficiency targets andimprovements throughout various parts of the system when compared to ahomogeneous computing architecture that employs CPUs for general purposecomputing.

FIG. 13 shows a block diagram of a system 1300 in at least oneembodiment of the present technology. The system 1300 includes acomputing device 1302, an input device 1304, and an output device 1306.The system is configured to exchange information between the computingdevice 1302 and input device 1304 and between the computing device 1302and output device 1306 (e.g., via wired and/or wireless transmission).For example, a network (not shown) can connect one or more devices ofsystem 1300 to one or more other devices of system 1300. In one or moreembodiments, the system 1300 is useful for outputting to output device1306 a design 1350 to output device 1306 for a design space 1352. Insome embodiments, the output device 1306 may be one or more of a displaydevice 1326, a printer 1328 or another device not shown (e.g., storagedevice or buffer).

Computing device 1302 includes an input interface 1308, an outputinterface 1310, a computer-readable medium 1312, and a processor 1314.In other embodiments, fewer, different, and additional components can beincorporated into computing device 1302.

The computing device 1302 receives information from input device 1304via input interface 1308. For instance, as shown in FIG. 13 theinformation received by input interface 1308 includes informationdescribing, representing or defining design space 1352 (e.g., a boundaryfor design space 1352). Alternatively or additionally, the informationreceived by input interface 1308 includes information describing,representing, or defining a categorical factor 1354 for the designspace. A categorical factor describes a design option at a design pointfor a design space. A categorical factor can have different designoptions represented by a level (e.g., a first level 1356 and a secondlevel 1358). Of course categorical factor 1354 can have other levels notshown in FIG. 13 . In other embodiments, multiple categorical factorscould be received by input interface 1308 for the design space 1352,with one or more levels for each of the categorical factors.

In one or more embodiments, the input device 1304 is one or more devicesfor user entry (e.g., entry of a formula to define the boundary of thedesign space 1352) into the system 1300. For instance, the input device1304 could include one or more of a mouse 1320 or a keyboard 1322.Alternatively or additionally, the input device 1304 includes a display,a track ball, a keypad, one or more buttons, a sensor, a phone, etc.Input interface 1308 in the same or different embodiments furtherprovides an interface for receiving information from another device ormachine such as a computing system 1324.

The computing device 1302 outputs information to output device 1306 viaoutput interface 1310. Output interface 1310 provides an interface foroutputting information (e.g., information representing design 1350) forreview by a user and/or for use by another application or device ormultiple applications or devices. For example, output interface 1310interfaces with various output technologies including, but not limitedto, display 1326 and a printer 1328. In the same or differentembodiments, output interface 1310 with a device for data storage ofoutput information.

In an alternative embodiment, the same interface supports both inputinterface 1308 and output interface 1310. For example, a touch screenprovides a mechanism for user input and for presentation of output tothe user. Alternatively, the input interface 1308 has more than oneinput interface that uses the same or different interface technology.Alternatively or additionally, the output interface 1310 has more thanone output interface that uses the same or different interfacetechnology.

Computer-readable medium 1312 is an electronic holding place or storagefor information so the information can be accessed by processor.Computer-readable medium 208 can include, but is not limited to, anytype of random access memory (RAM), any type of read only memory (ROM),any type of flash memory, etc. such as magnetic storage devices (e.g.,hard disk, floppy disk, magnetic strips), optical disks (e.g., compactdisc (CD), digital versatile disc (DVD)), smart cards, flash memorydevices, etc.

Processor 1314 executes instructions (e.g., stored at the computerreadable medium 1312). The instructions can be carried out by a specialpurpose computer, logic circuits, or hardware circuits. In one or moreembodiments, processor 1314 is implemented in hardware and/or firmware.Processor 1314 executes an instruction, meaning it performs or controlsthe operations called for by that instruction. The term “execution” isthe process of running an application or the carrying out of theoperation called for by an instruction. The instructions can be writtenusing one or more programming language, scripting language, assemblylanguage, etc. Processor 1314 operably couples with input interface1308, with output interface 1310 and with computer readable medium 1312to receive, to send, and to process information. Processor 1314 in oneor more embodiments can retrieve a set of instructions from a permanentmemory device and copy the instructions in an executable form to atemporary memory device that is generally some form of RAM.

In one or more embodiments computer-readable medium 1312 storesinstructions for execution by processor 1314. For example,computer-readable medium 1312 could comprise instructions for aclustering application 1360 for forming clusters of potential designpoints in the design space 1352, allocation application 1362 forallocating levels assigned to categorical factors in the design space,and modification application 1364 for modifying design points in thedesign space. In other embodiments, fewer, different, or additionalapplications can be stored in computer-readable medium 1312. Forinstance, applications could be stored to generate information, relevantto the designs space 1352 or categorical factor 1354 rather than or inaddition to receiving information from input device 1304.

In one or more embodiments, one or more applications stored oncomputer-readable medium 1312 are implemented in software (e.g.,computer-readable and/or computer-executable instructions) stored incomputer-readable medium 1312 and accessible by processor 1314 forexecution of the instructions. The applications can be written using oneor more programming languages, assembly languages, scripting languages,etc. The one or more application can be integrated with other analytictools. In one example, clustering application 1360, allocationapplication 1362, and modification application 1364 are integrated dataanalytics software application and/or software architecture such as thatoffered by SAS Institute Inc. of Cary, N.C., USA. Merely forillustration, the applications are implemented using or integrated withone or more SAS software tools such as JMP®, Base SAS, SAS® EnterpriseMiner™, SAS/STAT®, SAS® High Performance Analytics Server, SAS® VisualData Mining and Machine Learning, SAS® LASR™ SAS® In-Database Products,SAS® Scalable Performance Data Engine, SAS® Cloud Analytic Services,SAS/OR®, SAS/ETS®, SAS® Inventory Optimization, SAS® InventoryOptimization Workbench, SAS® Visual Analytics, SAS® Viya™, SAS In-MemoryStatistics for Hadoop®, SAS® Forecast Server, and SAS/IML® all of whichare developed and provided by SAS Institute Inc. of Cary, N.C., USA.

One or more applications stored on computer-readable medium 1312 can beimplemented as a Web application. For example, an application can beconfigured to receive hypertext transport protocol (HTTP) responses andto send HTTP requests. The HTTP responses may include web pages such ashypertext markup language (HTML) documents and linked objects generatedin response to the HTTP requests. Each web page may be identified by auniform resource locator (URL) that includes the location or address ofthe computing device that contains the resource to be accessed inaddition to the location of the resource on that computing device. Thetype of file or resource depends on the Internet application protocolsuch as the file transfer protocol, HTTP, H.323, etc. The file accessedmay be a simple text file, an image file, an audio file, a video file,an executable, a common gateway interface application, a Java applet, anextensible markup language (XML) file, or any other type of filesupported by HTTP.

The design 1350 has continuous variables for the design space 1352represented graphically as the two-dimensions of the design 1350 and canhave one or more categorical factors for the design space, representedgraphically by the levels 1356 and 1358 distributed in the design 1350.Factors and variables are used interchangeably herein. In one or moreexamples, a categorical factor has different levels that representdiscrete values or options for the design space in contrast to acontinuous variable that describes a range of possible values oroptions. The range can be infinite (e.g., taking on infinite values in abounded dimension by the constraints of an experiment such as the totallength, width, or height of an object for an experiment).

In one or more embodiments, the system 1300 implements a method asdescribed herein (e.g., a method shown in FIGS. 14A-14B, 15 and 17 ) foroutputting or storing a design (e.g., design 1350).

FIGS. 14A-14B illustrates a flow diagram for a computer-implementedmethod 1400 that can be used to select design points in various designspace. For example, the method 1400 could be used to select designpoints in design space 1352 of FIG. 13 , design space 1600 of FIGS.16A-J, or design space 1800 of FIGS. 18A-F. The method 1400 of FIG. 14Awill be described with reference to FIGS. 16A-J as an exampleimplementation of method 1400.

The method 1400 illustrates an operation 1401 in which informationdefining a boundary for a design space (e.g., design space 1600) isreceived (e.g., at computing device 1302 of FIG. 13 ). Informationdefining a boundary for a design space could explicitly or implicitlydefine the boundary or define all or a portion of the boundary. Forexample, the information could be one or more formula explicitlydefining all or a portion of the points on the boundary of a designspace. In another example, the information could include informationabout the design space for implicitly defining the boundary (e.g., ashape of the design space, such as a rectangle, a length of a side ofthe design space and/or a total volume of the design space). Moreexample embodiments of information defining a boundary for a designspace are defined herein.

In an operation 1402, representative points are generated (e.g., 1602A-P) as illustrated in FIG. 16A. Each representative point isdistributed on and/or within the design space and represents a potentialdesign point for the design space. In one or more embodiments, therepresentative points may be distributed randomly in the design space.

In operations 1403-1405 various features or properties are determinedfor the design space. For instance, representative points can beassigned or otherwise placed in one or more groups, which can be denotedas clusters. One of ordinary skill in the art will appreciate that theseoperations could be performed in any order or could be performed earlierin the method 1400.

In an operation 1403, primary clusters are determined. The primaryclusters are each groups of the representative points (e.g., eachprimary cluster contains a different set of the representative points).For instance, as described in operation 1403, the different sets ofrepresentative points may be different because they are mutuallyexclusive of representative points in other primary clusters of thedesign space. FIG. 16B shows as an example the representative points1602A-P of design space 1600 grouped into four primary clusters 1604A-Dwith different sets of representative points that are mutuallyexclusive. Of course, the representative points could be groupeddifferently (e.g., in different amounts of primary clusters and/or withdifferent numbers of representative points). These clusters are referredto as primary clusters in this example method 1400 and in otherembodiments herein only for ease of explanation. Of course, one ofordinary skill in the art will appreciate that the primary clusterscould be referred to differently. Further, these primary clusters arethe first grouping in method 1400. However, representative points couldhave been grouped previously (e.g., a design space could itself bedefined by a cluster of representative points) or a design space couldhave additional or different clustering defined for representativepoints in a design space.

In an operation 1404, a categorical factor for the design space isdetermined. As explained above, a categorical factor has differentlevels that represent discrete values or options for the design space.The determined categorical factor defines a design category for thedesign space, which means that the discrete values or options will bewithin this design category. For instance, in the background example ofa bottle, a design category could be material types for the bottle, withdifferent options including glass, metal, ceramic, and/or plastic.Method 1400 is described with reference to a single categorical factoras an example, but one of ordinary skill in the art will appreciate thatin other embodiments additional categorical factors are determined. Moreexamples of categorical factors will be described in the context ofother example embodiments below.

In one or more embodiments, a categorical factor is already input orotherwise generated for the design space and determining the categoricalfactor includes determining it for an operation in the method 1400. Inone or more embodiments, a categorical factor is determined, input orotherwise generated earlier in method 1400. For example, the number ofcategorical factors and/or the number of levels for the categoricalfactors can be used to determine a total number of primary clustersdetermined in 1403. Alternatively, in other embodiments, the categoricalfactor is determined, input, or otherwise generated later in method1400, e.g., for operations of method 1400 described below.

In an operation 1405, at least two levels for the categorical factor forthe design space are determined. Each of the levels defines differentdesign options in the design category for the design space. As explainedabove if the categorical factor is a material type for a bottle, onelevel could be plastic and another level could be glass. More examplesof levels will be described in the context of other example embodimentsbelow. In one or more embodiments, a level for a categorical factor isalready input or otherwise generated for the design space anddetermining the level includes determining it for an operation in themethod 1400. In one or more embodiments, a level is determined, input orotherwise generated earlier in method 1400. Alternatively, in otherembodiments, the level is determined, input, or otherwise generatedlater in method 1400, e.g., for operations of method 1400 describedbelow.

In one or more embodiments, the representative points are greater thanthe number of desired design points for the design space. Operations1406-1410 describe a series of operations for each of respective orselected primary cluster of the primary clusters to select the designpoints in the design space out of the representative points.

In operation 1406, a primary cluster from the primary clusters isselected. In operation 1407, a plurality of sub-clusters containingdifferent subsets of a respective set of the representative points isdetermined. For instance, as described in operation 1407, the differentsets of representative points are different because they are mutuallyexclusive of representative points in other sub-clusters of a selectedprimary cluster of the primary clusters. For example, as shown in FIG.16C, primary cluster 1604A has sub-clusters 1606A and 1606B. In one ormore embodiments, the total number of sub-clusters within a primarycluster is the maximum number of levels for any of the categoricalfactors defined for the design space.

In operation 1408, a design point is selected from each sub-cluster ofthe selected primary cluster based on a first criterion representingseparation distance between design points in the design space. Forexample, as shown in FIG. 16D, representative points 1602B and 1602E areselected as the design points for sub-clusters 1606A and 1606B,respectively of cluster 1604A. One example of a first criterionrepresenting separation between design points is a centroid criterion,which places a design point at the center of each sub-cluster. Anotherexample of a first criterion is a MaxPro criterion (this will beexplained in more detail later in the application) that finds a designpoint in a sub-cluster that minimizes the MaxPro Criterion.

In operation 1409, the levels of the categorical factor are allocated toeach selected design point in the selected primary cluster. For example,as shown in FIG. 16E there is a first level 1608 (represented by atriangle) assigned to design point 1602B and a second level 1610(represented by a square) assigned to design point 1602E.

Operation 1410 is used to determine whether there are more primaryclusters remaining for completing the operations 1406-1409. If there arestill remaining primary clusters, the method returns to operation 1406to select a different primary cluster of the remaining primary clusters.If the operations reach the end of the primary clusters, the methodprecedes as shown in FIG. 14B. If there are k primary clusters and msub-clusters, the operations should produce km design points.

The initial allocation of the levels to the design points of the primarycluster can be random. The use of random assignment means that designpoints with the same level for a categorical factor can end up too closeto each other than would be desired.

FIG. 14B shows a method for modifying a sub-design of a level for acategorical factor to improve the separation distance between designpoints with the same level for a categorical factor.

As shown in FIG. 14B, in operation 1411, an initial sub-design thatrepresents the selected design points in the design space allocated agiven level of the categorical factor is obtained. In operation 1412,the initial sub-design is modified by increasing the separation distancebetween design points allocated a same level of the categorical factorin the design space. For example, the modification could involve levelswapping, which is described in more detail in reference to FIGS. 15 and16A-J. Alternatively, or additionally the modification could involvedesign point replacement, which is described in more detail in referenceto FIGS. 17 and 18A-F. In operation 1413, a modified design for thedesign space is output. The modified design represents locations forobtained design points in the design space in response to modificationof the initial sub-design.

The method 1400 can be carried out by system 1300 or other systems notshown. In one or more embodiments involving methods described herein,additional, fewer, or different operations can be performed depending onthe embodiment. For instance, the method 1400 could include receiving(e.g., via user input) one or more categorical factors for the designspace before operation 1401 or later in the method 1400 (e.g., prior tooperation 1407).

Although some of the operational flows in methods described herein arepresented in sequence, the various operations may be performed invarious repetitions, concurrently (in parallel, for example, usingthreads and/or a distributed computing system) and/or in other ordersthan those that are illustrated. For example, operation 1404 could occurbefore operation 1402.

FIG. 14A and FIG. 15 illustrate a flow diagram of a method 1400 in atleast one embodiment involving level swapping. As shown in FIG. 15 , theoperation 1412 can involve a series of operations for modifying aninitial sub-design by swapping a first level allocated to a firstselected design point and a second level allocated to a second selecteddesign point of a primary cluster of the design space. Level swappingthen, for instance, involves replacing the first level allocated to thefirst selected design point and allocating it to the second selecteddesign point; and replacing the second level allocated to the secondselected design point and allocating it to the first selected designpoint. Level swapping can be considered modifying, switching, replacing,or otherwise changing a level associated with a design point.

As shown in FIG. 15 , operation 1501 involves selecting candidate designpoints for level swapping, i.e., a first design point allocated a firstlevel and a second design point allocated a second level. Operations1502 and 1503 involve swapping the levels by removing the first levelallocated to the first selected design point and allocating it to thesecond selected design point (1502), and removing the second levelallocated to the second selected design point and allocating it to thefirst selected design point (1503). As shown the operations could occurin parallel or alternatively sequentially. In operation 1504, an updatedsub-design is generated based on the swapped allocation. A choice ismade in operation 1505 as to whether to adopt the swapped allocation,and if so, replace the initial sub-design with the updated sub-design inoperation 1506.

In operation 1507, a choice is made as to whether to end the process orcontinue picking candidate design points in the design space for levelswapping. This choice can be automated or manual. For instance, in oneor more embodiments, conditions are set to automatically decide when toend the process. In other embodiments, a user can manually stop theprocess (e.g., via user input).

Operations 1501-1507 could be used to find candidate design points ineach primary cluster of the design space and to determine whether tochange a level allocated to a design point of a sub-cluster of each ofthe primary clusters of the design space. A condition could be set toiterate a fixed number of times (e.g., 10 iterations) or through eachprimary cluster a fixed number of times.

Alternatively or additionally, a condition could be set to performoperations 1501-1507 until no improvement is found. In one embodiment,improvement is determined by determining an initial average representingseparation of design points in the design space. For instance, for eachof the levels of the categorical factor, computing an initial computedcriterion representing the separation distance between design pointsallocated a same level in the design space to obtain a plurality ofcomputed criterion, one for each of the levels of the categorical factorand then averaging the initial computed criterion.

In one or more embodiments, improvement is determined by comparing anupdated average when levels are swapped to the initial average. Forinstance, the updated average is performed by determining a firstcandidate design point and a second candidate design point of selecteddesign points of a primary cluster in the design space. A swappedallocation is obtained for the design space by replacing a levelallocated to the first candidate design point for the categorical factorwith a level allocated to the second candidate design point for thecategorical factor. An updated sub-design is determined based on theswapped allocation. An updated average representing the separation ofdesign points in the design space according to the swapped allocation isobtained. If a difference between the updated average and the initialaverage indicates, the updated sub-design achieves a greater separationof design points of a same level than the initial sub-design, theinitial sub-design of the design space is modified by replacing theinitial sub-design with the updated sub-design; and replacing theinitial average with the updated average. In one or more embodiments,level-swapping advantageously allows improved modeling on a particularsub-design level. In one or more other embodiments, level swappingallows for improvement of the full design such that the updated designachieves a greater separation distance for a majority of selected designpoints in the design space even though it may diminish separationdistance for some of the selected design points in the design space.

FIGS. 16A-16J illustrates a block diagram of design point selection inat least one embodiment involving level swapping (e.g., in the method1400 described in FIG. 14A-14B and FIG. 15 ). As explained the method1400 of FIG. 14A can be used to obtain a design for a design space(e.g., design 1650 shown in FIG. 16E). FIGS. 16F-16J show an example oflevel swapping by moving through each primary cluster of the designspace 1600 to increase or improve the separation distance between designpoints allocated a same level of the categorical factor in the designspace 1600.

As shown in FIG. 16F, the levels allocated to the sub-clusters incluster 1604A are swapped producing the swapped allocation 1652. FIG.16G shows an example where it is determined that this swapped allocation1652 did not improve the separation distance, so in FIG. 16G the swappedallocation 1654 shows that the levels in cluster 1604A have returned totheir form in FIG. 16E and levels are swapped in cluster 1604B.

FIG. 16H shows an example where it is determined that the swappedallocation 1654 did improve the separation distance, so in FIG. 16H theswapped allocation 1656 shows that the levels in cluster 1604B haveadopted the levels shown in FIG. 16G and levels are swapped in cluster1604C. This swapped allocation 1656 has resulted in a modification oftwo different sub-designs, one for level 1608 and one for level 1610.

FIG. 16I shows an example where it is determined that the swappedallocation 1656 did improve the separation distance, so in FIG. 16I theswapped allocation 1658 shows that the levels in cluster 1604C haveadopted the levels shown in FIG. 16H and levels are swapped in cluster1604D.

FIG. 16J shows a representation of an output modified design 1660 forthe design space 1600. The modified design represents locations forobtained design points in the design space in response to modificationof the initial sub-designs for level 1608 and level 1610.

Level swapping as described with reference to FIGS. 14A, 15, 16A-J isjust one way to modify an initial sub-design by increasing separationbetween design points allocated a same level of the categorical factorin the design space. In one or more embodiments, it effectively concernsitself first with the space-fillingness of the overall design for thecontinuous factors, and then works on the sub-designs for each level ofthe categorical factors. Alternatively, or additionally the modificationcould involve design point replacement.

FIG. 17 illustrates a flow diagram of a method 1400 in at least oneembodiment involving design point replacement. As shown in FIG. 17 , themethod 1400 can modify an initial sub-design from operation 1411 by, fora primary cluster, reselecting one or more design points of therepresentative points to replace corresponding ones of design points inthe initial sub-design. In one or more embodiments, the design pointreplacement minimizes a second criterion representing the separationdistance between design points allocated a given level. In one or moreembodiments, the first and second criterion can be considered a distancemetric because they represent the separation distance between designpoints.

In one or more embodiments, the second criterion is different than thefirst criterion in operation 1408. For example, the first criterion is aMaxPro criterion and the second criterion is a modified MaxPro criterionthat accounts for a received weight.

In one or more embodiments, the MaxPro criterion is represented by:

${\min\limits_{D}}_{\psi(D)} = \left\{ {\frac{1}{\left. (_{2}^{n} \right)}{\sum\limits_{i = 1}^{n - 1}{\sum\limits_{j = {i + 1}}^{n}\frac{1}{\prod\limits_{l = 1}^{p}\left( {x_{il} - x_{jl}} \right)^{2}}}}} \right\}^{1/p}$where: ψ(D) is the MaxPro criterion;

-   -   i, j, l are integer counters;    -   n is an integer number of primary clusters for the design space;    -   p is an integer number of continuous variables for the design        space; and    -   x_(ab) is an entry in row a and column b of a matrix.

In one or more embodiments, the modified MaxPro criterion accounts for areceived weight w and is represented by:

${\min\limits_{D}}_{\psi(D)} = \left\{ {\frac{1}{\left. (_{2}^{n} \right)}{\sum\limits_{i = 1}^{n - 1}{\sum\limits_{j = {i + 1}}^{n}\frac{I_{w}\left( {c_{i} = c_{j}} \right)}{\prod\limits_{l = 1}^{p}\left( {x_{il} - x_{jl}} \right)^{2}}}}} \right\}^{1/p}$where: ψ(D)_(w) is the second criterion that is a modified MaxProcriterion;

-   -   i, j, l are integer counters;    -   n is an integer number of primary clusters for the design space;    -   p is an integer number of continuous variables for the design        space;    -   x_(ab) is an entry in row a and column b of a matrix; and    -   I_(w)(c_(d1)=c_(d2)) is an indicator variable that is w if a        categorical level c_(d1) allocated to a design point d1 and a        categorical level c_(d2) allocated to a design point d2 are the        same, and 1 otherwise.

In one or more embodiments, a matrix is used for either or bothcriterion to map the design space onto a matrix with rows and columns.For example, a row of a matrix represents a design point in the designspace and the columns represents a value in a particular dimension foreach of the design points. For example, if a design space is in a threedimensional space with points (0,0,0), (0,1,0), and (1,1,1), the matrixX could be represented as:

$X = \begin{bmatrix}0 & 0 & 0 \\0 & 1 & 0 \\1 & 1 & 1\end{bmatrix}$Of course, one of ordinary skill in the art will appreciate that therows and columns for the matrix X can be reversed and that othercriterion can be used for the first and second criterion (e.g., acentroid approach can be used for the first criterion). Furthermore, thematrix may have various dimensions.

In operation 1701, a weight is received for tuning the second criterion(e.g., a modified MaxPro criterion as described herein).

In operation 1702, using the received weight, for each level of thecategorical factor, a design point location is selected in the designspace that is different than a design point location of the selecteddesign points for the primary clusters.

In operation 1703, a design point location of a selected design pointfor the primary clusters is replaced with the selected design pointlocation.

In operation 1704, a choice is made as to whether to end the process orcontinue picking candidate design points in the design space for designpoint replacement. This choice can be automated or manual. For instance,in one or more embodiments, conditions are set to automatically decidewhen to end the process. For instance, operations 1702-1704 could beused to find candidate design points in each primary cluster of thedesign space and to determine whether to select a replacement designpoint location for a design point in a selected primary cluster thatincreases the separation distance between design points allocated alevel of the categorical factor in the design space. A condition couldbe set to iterate a fixed number of times (e.g., 10 iterations) orthrough each primary cluster a fixed number of times. Alternatively oradditionally, a condition could be set to perform operations 1702-1704until no improvement is found as explained herein.

The MaxPro criterion for operation 1408 when considering the continuousfactors has advantageous over other criterion including (1) that pointsare non-collapsing (no replication of points in lower dimensions); (2)the criterion forces points apart in each dimension because of the(x_(il)−x_(jl))² term in the denominator; (3) the criterion has goodlower-order projections across dimensions; and (4) faster computation.

Assuming a MaxPro criterion, the design when looking at just thecontinuous factors should ideally have a small MaxPro value and for eachlevel of a categorical factor, the sub-design for the continuous factorsshould have a small MaxPro value.

From a practical standpoint, if a categorical factor is included, it isnot uncommon that the categorical factor is of particular interest. Forthe approach described with level swapping, the emphasis is firstly onthe overall space-fillingness of the continuous factors. However, whenemphasis should be placed on the categorical factors, a practitioner isalso concerned that sub-designs on each level be as space-filling aspossible, and they are willing to sacrifice a bit on the overall design.If each sub-design is created separately and combined together, onewould end up with points too close to each other sacrificing too much ofthe overall design. One could try to optimize the MaxPro criterion foreach design separately, while at the same time minimizing the overallMaxPro criterion, but computationally it is advantageous to have asingle criterion computation. This technical advantage speeds upcomputations, and does not require considering each subdesignseparately. The design point replacement approach with modified MaxProallows one criterion to handle the choice of design points, but alsoallow flexibility to put weights on what is more important: the overalldesign or the projected sub-designs.

FIGS. 18A-18 illustrates a block diagram of design point selection in atleast one embodiment involving design point replacement. FIG. 18A showsgenerated representative points distributed in the design space 1800.FIG. 18B shows the representative points in design space 1800 clusteredinto primary clusters 1804A-1804D. With either design point replacementor level swapping, the clustering can produce primary clusters withdifferent amounts of representative points as shown in FIG. 18B or thesame amount of representative points as shown in FIG. 16B.

As shown in FIG. 18C, the primary clusters 1804 A-D of design space 1800are separated into sub-clusters for allocation of two levels of acategorical factor to each of the primary clusters. For example, primarycluster 1804 A is separated into sub-clusters 1806A and 1806B. A designpoint is selected from the representative points in each of thesub-clusters and allocated one of the two levels. For example, designpoint of level 1 1808 A is selected in sub-cluster 1806 B.

As shown in FIG. 18D, the location of design point of level 1 1808 A ismoved to a different location of a representative point in design space1800 (e.g., in the same sub-cluster 1806 B). In one or more embodiments,each reselected design point replaces a corresponding initial designpoint in a same sub-cluster. As shown in FIG. 18E since the reselecteddesign point 1808 A increases or improves the separation of designpoints allocated of level 1 in a sub-design for level 1, the modifiedsub-design is selected and another design point of level 1 1808 B isselected in sub-cluster 1806 D of cluster 1804 B. Alternatively, oradditionally modification could involve modifying another sub-design fora different level. FIG. 18F shows modification of a design pointallocated a different level (level 2) 1810 A in sub-cluster A.

As shown in FIGS. 19A-B, design point replacement can be used inconjunction with level swapping. For example, FIG. 19A shows a design1850 from the design point replacement as shown in FIGS. 18A-F. FIG. 19Bshows level swapping in primary cluster 1804 C from the allocationproduced by design point replacement shown in FIG. 19A.

FIGS. 20A-20B show an alternative way to use design point replacement inconjunction with level swapping. FIG. 20A shows a design 1600 for designspace 1600 from level swapping as shown in FIGS. 16A-16J. FIG. 20B showsdesign point replacement for primary cluster 1604 C from the allocationproduced by the level swapping. Of course, one of ordinary skill in theart will appreciate other ways to use design point replacement inconjunction with level swapping (e.g., alternating between thesetechniques). In one or more embodiments, a modification of an initialsub-design involves performing two modifications one of level-swappingand one of design point replacement.

In one or more embodiments, representative points are clustered intoprimary clusters and sub-clusters. FIG. 16A and FIG. 18A show clusteringrepresentative points close in proximity. FIG. 21 shows an alternativemanner of clustering, in which the clustering results in intertwinedclusters. FIG. 21 shows four different clusters 2102, 2104, 2106 and2108 in design space 2100 with some sections of a cluster close together(e.g., representative points in an A section or B section) and somesections of the cluster separated (e.g., sections A and B are separatedfrom each other).

There are many techniques that can be employed for clustering herein.One example, is using hierarchical clustering. The hierarchicalclustering method starts with each observation forming its own cluster.At each step of the hierarchical clustering method, the distance betweeneach pair of clusters is computed, and clusters are combined that areclosest together. This process continues until all the observations arecontained in a particular number of clusters (e.g., the required numberof primary clusters). Hierarchical clustering is also calledagglomerative clustering because of the combining approach that it uses.

Another example include k-means clustering. In k-means clustering, aspecified number of clusters are constructed using an iterativealgorithm that partitions the observations. The method, called k-means,partitions observations into clusters so as to minimize distances tocluster centroids. One of ordinary skill in the art will appreciateother means of clustering including (e.g., normal mixtures, partitioningmethods, fuzzy clustering, density-based clustering, model-basedclustering and other clustering described herein).

FIG. 22 shows different example design spaces for one or moreembodiments described herein. The design spaces 2200, 2204, 2206, and2208 are examples of design spaces that are non-rectangular. Designspace 2202 has borders of sides that are rectangular for a threedimensional shape. In one or more embodiments, a computing device (e.g.,computing device 1302 of FIG. 13 ) displays, on a display device (e.g.,display 1326) a graphical user interface for user entry of informationdefining the boundary for the design space. For instance, the user inone or more embodiments enters information that may represent a formulafor one or more portions of the boundary of a design space.Alternatively, the graphical user interface provides various selections(e.g., a circle, a pentagon) for a design space and the user can selecta design space. Alternatively, the graphical user interface allows auser to upload a file with individual user points or an image (e.g., animage of the boundary of the State of North Carolina) that a computingdevice then translates into a boundary for the design space.

FIGS. 23A-23B shows other example graphical user interfaces forselection of criteria related to one or more categorical factors and/orlevels associated with a categorical factor for the design space. Forinstance, the selection could describe all the categorical factors. Asshown in FIG. 23A, the graphical user interface 2300 displays a text box2302 for entry of a weight to describing tuning of design points thathave a same categorical level. In one or more embodiments, the selectionis related to the representative design points that will be allocated alevel of a categorical factor. In one or more embodiments, the selectionis related to controlling the particular categorical factors or levels.For example, as shown in FIG. 23B a graphical user interface 2350 allowsa user to select categorical factors of combinations that are notallowed in particular situations (e.g., certain values of a categoricalfactor that cannot be used when a continuous variable is within acertain range). For example, as shown in FIG. 23B, the user has selecteda disallowed combinations filter, criteria 2352. A level selection 2356in FIG. 23B allows a user to specify the levels for a categorical factorX4 for the space that cannot be used when continuous variable X3 has thevalues selected using the spectrum 2354. The spectrum 2354 allows theuser to adjust the range for a continuous variable X3 in this disallowedcombination. Alternatively, a user could use a “Disallowed Combinations”script to upload a script to automatically choose points in theallowable design space rather than use user entry of continuous orcategorical factors. One of ordinary skill in the art will appreciatedifferent selections that could be presented to selected or otherwisedescribe criteria related to aspects of the design space (e.g.,continuous variables, categorical factors, and/or levels associated witha categorical factor for the design space).

FIG. 24A shows an example embodiment involving a design space 2400 witha non-rectangular boundary (i.e., the boundary of the State of NorthCarolina). The design space 2400 is two dimensional with two continuousvariables for design 2401 defining the latitude and longitude of thedesign space. The design 2401 has two sub-designs, one for each level(L1, L2) of a categorical factor (categorical factor X3) in which levelswapping is used. Design space 2400 is useful for experiments in theState of North Carolina. For example, design space 2400 in one or moreembodiments is used for an experiment in which air quality is testedacross the State of North Carolina. It would be good to distribute airquality sensors distributed uniformly across North Carolina both withinand at the border of the state. There may be more than one type orsupplier of air quality sensors, and it would be good to also evenlydistribute the different types of air quality sensors across the statewithout having overlapping air quality sensors of different types.Alternatively or additionally, an experiment using the model could havephysical aspects (e.g., collecting data with physical sensors) andcomputer-simulated aspects (e.g., collecting data with simulators). Thetype of the air quality sensor or the method of data collection(physical or computer simulator) could then be categorical factor X3 forthe design 2401 with the different types allocated a level 1 (L1) and alevel 2 (L2). Thus, design 2401 is useful for an experiment withcategorical factors combined with continuous variables. Two aspects areconsidered for the design. First, the design when looking at just thecontinuous factors should have good space-filling properties. Second,for each level of a categorical factor, the sub-design for thecontinuous factors should have good space-filling properties. Design2401 was produced using a level-swapping approach according to one ormore embodiments described herein.

FIG. 24B shows the sub-designs separated with sub-design 2402 showingjust the distribution of design points allocated level 1 (L1) for acategorical factor X3 and sub-design 2404 showing just the distributionof design points allocated level 2 (L2) for a categorical factor X3.Generally, the sub-designs have achieved good distribution of the designpoints. However, as shown, in regions 2406, 2408 and 2410 there arestill some regions of the design space that have clumped togethercreating coverage holes in the distribution.

FIG. 24C shows an example embodiment in which design point replacementis used for the same design space 2400. The modified MaxPro Criterionand a received weight of 4 is used to produce the design 2411. FIG. 24Dshows the sub-designs for design 2411 separated with sub-design 2412showing just the distribution of design points allocated level 1 (L1)for a categorical factor and sub-design 2414 showing just thedistribution of design points allocated level 2 (L2) for a categoricalfactor X3. Regions 2406, 2408 and 2410 in this example show a betterdistribution for each of the sub-designs than in FIG. 24B that used onelevel swapping approach. In different embodiments, the level-swappingapproach could produce a better distribution than design pointreplacement or a combination of approaches could provide a betterdistribution than either approach alone.

FIG. 24E shows a side-by-side comparison of the design 2401 and design2411. Since design 2411 was produced with level swapping and themodified MaxPro criterion, it can be referred to as a weighted design indistinction from design 2401, which can be considered unweighted. FIG.24F shows a side-by-side comparison of the sub-designs 2402, 2412, 2404and 2414 of FIGS. 24B and 24D.

One or more embodiments herein are useful for multi-dimensional designspaces that test multiple continuous factors. FIGS. 25A-25B show anexample of a space-filling design for 200 design points or computationalruns with 6 continuous factors (X1-X6) and a two-level categoricalfactor. The distribution of points in each of the dimensions can berepresented graphically by two-factor projections of the full designshown in FIG. 25B. FIG. 25A shows a scatterplot 2500 for athree-dimensional projection for the full design. Examples of continuousfactors for an experiment are experiment dependent (e.g., physicaldimensions, time, hardness, clarity, brightness, etc.).

FIGS. 26A-26D shows each of the sub-designs for the example in FIG.25A-25B. FIG. 26A represents a sub-design for a first level graphicallyas a scatterplot 2600 for a three-dimensional projection for the subdesign. FIG. 26B represents a sub-design for a first level graphicallyas a scatterplot 2602 with two-factor projections for the sub-design.FIG. 26C represents a sub-design for a first level graphically as ascatterplot 2650 for a three-dimensional projection for the sub design.FIG. 26D represents a sub-design for a first level graphically as ascatterplot 2652 with two-factor projections for the sub-design.

One or more embodiments are useful for space filling a model of athree-dimensional physical object like a physical bowl as shown by thescatterplot 2700 in FIG. 27 with a categorical factor X4 of two levelsL1 and L2. FIG. 28A shows the sub-design 2800 for L1, and FIG. 28B showsthe sub-design 2850 for L2. This is useful for designing experiments ona physical object or model of a physical object (e.g., testing strain ona physical object, such as a physical bowl). Experiments on physicalobjects are useful for a variety of applications. For instance,space-filling designs for modeling heat transfer in an object are usefulfor building diagnostics, inspection of automotive and airplane parts(e.g., finding and quantifying defects), medical screening, inspectionof artwork and historical monuments. For example, in the agriculturesegment, space-filling designs are useful for modeling of produceobjects which can be helpful for screening agriculture products (e.g.,based on heat transfer, biological variability, fungi infestations,etc.).

One or more embodiments herein describe approaches that would be usefulfor designing a design space (e.g., for the experiment in FIGS.24A-24F). In particular, one or more embodiments herein are useful forspecifying categorical factors for allowable regions of a nonregularregion (e.g., the State of North Carolina). One or more embodimentsherein are useful for quickly designing a design space. From aprocessing standpoint, one or more criterion used herein are cheap tocompute because they require less processing. For instance, with designpoint replacement using a modified or weighted MaxPro criterion, inconsidering moving a current design point with another point in aprimary cluster, a method only needs to consider the other design pointsand the change in the weighted MaxPro criterion, which can be done inlinear time. This also allows it to be threaded—each potential point canbe considered separately and passed with the current design. This methodcan be combined with other techniques such as hierarchical clusteringusing the Fast Ward method of JMP® to achieve greater speed.

One or more embodiments herein achieve greater efficiency for knowntechniques of distributing design points. For example, instead of tryingto optimize each subdesign and the full design simultaneously anddeciding on appropriate weights in that regard, the modified MaxProcriterion uses a single tuning parameter for the weight, whereby a usercan balance which is more important—the space-fillingness on the fulldesign or the space-fillingness on the sub-designs.

One or more embodiments herein allow flexibility in run size (e.g.,specifying the number of design points in the region) or in weighted orotherwise valuing distribution of categorical factors over overalldistribution.

One or more embodiments allow flexibility in constraining the designspace by allowing constraints on the design space, continuous variablesfor the design space, and categorical factors as described herein.

One or more embodiments herein provide advantages over other approachesfor distributing design points in a design space. For example, generalsliced Latin hypercube is an approach that approximates a design as arectangle or cube with multiple layers, at each of which there aremultiple Latin hypercube designs that can be sliced into smaller Latinhypercube designs at the next layer. Due to constraints based on thisapproaches underlying mathematical computations, this approach isinflexible for run size because a certain amount of runs is required toperform the computations, is not amenable to nonregular or rectangularregions, and cannot place weight on valuing distribution of a sub-designover the overall design.

A useful metric for comparing the performance of general sliced LatinHypercube (SLHD) to an approach described herein which can be referredto as fast flexible filling (FFF) is shown below.

${Mm_{q}} = {\min\limits_{r}\left( {\frac{1}{\left. (_{2}^{n} \right)}{\sum\limits_{i = 1}^{n - 1}{\sum\limits_{j = {i + 1}}^{n}\frac{1}{d_{qr}^{2q}\left( {x_{i},x_{j}} \right)}}}} \right)}^{- \frac{1}{2q}}$where: Mm_(q) is a maximum measure for projection dimension q;

-   -   r is the worst-case projection of dimension q    -   d_(qr) is the Euclidean distance between x_(i) and x_(j) in the        r projection of dimension q; and    -   n is the number of design points or run designs.

FIG. 29 shows a graphical comparison 2900 of the Mm_(q) values for FFFunweighted and SLHD. When comparing approaches, a larger value of Mm_(q)is preferred. Both approaches are applied to a 200-run design with 10continuous factors and a 4-level categorical factor across design spacesof different dimensions ranging from 1 to 10. Only in dimensions 1 and10, does SLHD performs better.

FIG. 30 shows a graphical comparison 3000 of the average Mm_(q) valuesfor all the sub-designs for SLHD and FFF weighted with weights rangingfrom 1 to 6. Both approaches are applied to a 200-run design with 10continuous factors and a 4-level categorical factor across design spacesof different dimensions ranging from 1 to 10. A smoother is applied tothe line's connecting the plotted points to get an idea of the fit. Forthe sub-designs, using a weight of 1 is notably worse than putting anyweight on improving sub-designs (i.e. a weight >1).

FIG. 31 shows a graphical comparison 3100 of the Mm_(q) value for theoverall design for SLHD and FFF weighted with weights ranging from 1 to6. Both approaches are applied to a 200-run design with 10 continuousfactors and a 4-level categorical factor across design spaces ofdifferent dimensions ranging from 1 to 10. For the overall-design, aweight of 1 is best, as expected since this does not favor thesub-designs over the designs, but if the categorical factor isconsidered important, the tradeoff is not extreme.

As shown in FIGS. 29-31 , one or more embodiments improve traditionalcomputer approaches to space-filling designs for experiments.Computations of Mm_(q) allow experimenters to try differing weights andcompare the designs to decide what tradeoffs are preferable inoptimizing the overall space-filling design or the space-filling designof individual sub-designs.

Space-filling designs are typically employed for experiments to selectseveral different inputs for a given factor distributed across thedesign space. For example, if the experiment is a strain experiment on abottle, the design space may be a model of a bottle, and a factor forthe experiment could be a location on the bottle to apply a test strain.The researcher will want to design the experiment to test severaldifferent locations to apply the strain on the bottle. Each of designpoints of the space-filling design could then correspond to a designcase in a design suite for designing the experiment according to thespace-filling design.

In other applications, it may be useful to provide a design that narrowsthe design of a system (i.e. a designed system) to a particular designcase or a limited number of the design cases. One or more embodimentsare directed to outputting a design case using a space-filling design asa starting point for selecting the design case. A design case can beselected that optimizes some criteria of interest for the system (e.g.,based on an evaluation of a response for the system). This approach isparticularly helpful when the possible options for a factor are veryhigh (e.g., when a factor can be assigned a value from one of a range ofcontinuous values).

FIG. 32 illustrates a block diagram of a system 3200 for outputting anindication 3270 of a selected design case using a space-filling design3252 (e.g., generated using a traditional or improved approach tospace-filling designs described herein). The system 3200 includes acomputing device 3202 useful for outputting an indication 3270 of aselected design case. In one or more embodiments, the computing device3202 is the same or different from computing devices described herein(e.g., computing device 1302). The indication 3270 of the selecteddesign case could be used to design a designed system (not shown in FIG.32 ).

The system 3200 is configured to exchange information related todesigning a designed system between devices (e.g., output device 3206)in the system 3200 (e.g., via wired and/or wireless transmission) and/ordevices in other systems described herein (e.g., system 1300). Forexample, a network (not shown) can connect one or more devices of system3200 to one or more other devices of system 1300. In one or moreembodiments, fewer, different, and/or additional components than showncan be incorporated into the system 3200 (e.g., components of system1300).

In one or more embodiments, the computing device 3202 receives designinformation 3254 related to designing a designed system (not shown inFIG. 32 ). For example, in one or more embodiments, the designinformation 3254 comprises factor information 3256 indicating multiplefactors for designing a system. The design of the system is designed byassigning one of respective possible options to each of the multiplefactors. In one example, a user of the system 3200 is designing afactory or computer model of a factory with products moving on aconveyor belt. The user designing the factory system many need todetermine how fast a conveyer belt in the factory operates or how manyunits go through a check point station before a quality check. Inanother example, the designed system could be a system employing analgorithm with values assigned for the factors affecting how thealgorithm is structured or used. One example of an algorithm is amachine learning algorithm where hyperparameters define the system bycontrolling the behavior of the machine learning system employing themachine learning algorithm.

In one or more embodiments, the design information 3254 comprises rangeinformation 3258 indicating a range for respective possible options forthe multiple factors. For example, a factor may be continuous like thespeed at which the products move on the conveyer belt, but constrainedto within certain reasonable speeds to ensure quality production. Forexample, the possible options for the speed could be a value between 1meter per second (m/s) to 2 m/s. A factor may be discrete. For example,a categorical factor is a discrete factor where levels correspond todifferent options within the category of the categorical factor. Anexample, of a categorical factor is different types of products on theconveyer belt, and the different levels could correspond to differentproduct types (e.g., metal, plastic, paper) and be within a range. Anexample of a nominal categorical discrete factor could be the number ofunits that pass through before a quality inspection and could beconstrained to a range with discrete numbers between 100 and 200 units.

In one or more embodiments, the range information 3258 defines a designspace. For example, if a factor defines a dimension of a design space,the range information for that factor limits the expanse of thatdimension. Representative design points along that dimension correspondto possible options for that factor.

In one or more embodiments, the design information 3254 comprises aspace-filling design 3252 for the design space (e.g., a space-fillingdesign constructed as described herein in which design points areselected from representative points within the design space). Aspace-filling design as described herein indicates selected designpoints in the design space. For instance, the space-filling design coulditself provide design cases corresponding to the selected design points.Each of the selected design points represents assigned options assignedto the multiple factors. The assigned options are assigned within theranges defined in the range information 3258.

As described in more detail above, in a space-filling design, points arespread out within the design space (e.g., in all dimensions). Points maybe spread out or separated from one another in the design space based ona criterion for separating the selected design points in the designspace.

In one or more embodiments, the input interface 3208 comprises one ormore features of an input interface described herein or is an inputinterface described herein (e.g., input interface 1308). In one or moreembodiments, the system 3200 comprises one or more devices from which toreceive or obtain the design information 3254. For example, the designinformation 3254 could be received from a user of the system 3200. Forexample, it is received via one or more input devices 3204 (e.g., usinga mouse 3220, keyboard 3222 and/or computing system 3224) or other inputdevices for providing information to a computing device.

Additionally or alternatively, the design information 3254 is obtainedfrom one or more default values stored in the computing device 3202(e.g., in computer-readable medium 3212) or generated by the computingdevice 3202. For example, in one or more embodiments, the computingdevice 3202 itself generates or stores a space-filling design 3252 asdescribed herein. In the case of the machine learning algorithm, thehyperparameters that control the machine learning algorithm may be knownor executed by the computing device 3202 and default factors and rangesfor those factors could be stored at the computing device 3202. Forexample, if the machine learning algorithm uses a tree algorithm afactor might indicate the maximum depth of a tree. Default values forthis factor could be a discrete nominal value between 3 and 9. The inputinterface 3208 may be an internal input interface or may receive thedesign information 3254 by receiving from the user of the system 3200accept an indication of one or more default factors or ranges stored atthe computer-readable medium 3212.

In one or more embodiments, the computer-readable medium 3212 comprisesone or more features of one or more computer-readable mediums describedherein or is one of computer-readable mediums described herein (e.g.,computer-readable medium 1312). In one or more embodiments, thecomputing device 3202 has a processor 3214 (e.g., processor 1314). Forinstance, the processor 3214 comprises one or more features of one ormore processors described herein or is one of processors describedherein (e.g., processor 1314).

In one or more embodiments, computer-readable medium 3212 storesinstructions for execution by processor 3214. For example,computer-readable medium 3212 comprises instructions for generating adesign suite, generating evaluations of design cases of the designsuite, and outputting an indication of a selected design case.

For example, in one or more embodiments, the computer-readable medium3212 comprises a design suite application 3240 that obtains aspace-filling design 3252 for a design space and generates, based on thespace-filling design, a design suite (e.g., an initial design suite).For instance, an initial design suite provides initial design casescorresponding to one or more of selected design points of thespace-filling design 3252 or other indications of design cases for theinitial design suite. Each element of a respective design case of theinitial design suite is one of the assigned options represented by aselected design point of the one or more selected design points. Forexample, if a design point for a factory design corresponds to aconveyor belt speed of 1.75 m/s, 125 units before a quality inspection,and a metal product line, then the design case would have an elementrepresenting the assigned speed of 1.75 m/s, an element representing the125 units, and a level associated with metal product line.

Alternatively or additionally, the computer-readable medium 3212comprises an evaluation application 3242 for generating evaluations ofdesign cases of a design suite (e.g., an initial design suite). In oneexample, the design cases are evaluated by modeling respective responsesof a simulated designed system according to each of the design cases.Each of the responses corresponds to an operation of the system definedby each element of a given respective initial design case of the initialdesign suite. A case can then be selected from these evaluations thatare predicted to optimize the response according to some measure ofinterest. For example, in the context of a machine learning algorithm,the modeling may be used to evaluate the predictions of the machinelearning algorithm. One metric that can be computed is a coefficient ofdetermination (R{circumflex over ( )}2) that provides the indication ofthe fit of a set of predictions by the machine learning algorithm toactual values. This metric traditionally provides a value between 0 forno fit and 1 for a perfect fit. A coefficient of determination could becomputed for each of the design cases, and one or more design cases thatare closest to a perfect fit or above some threshold set by anoptimality criterion can be selected for further evaluation or foroutput. Alternatively or additionally, this information is used togenerate a design suite.

In one or more embodiments, the computing device 3202 outputs anindication 3270 of a selected design case. The selected design case canbe based on the evaluations of design cases by the computing device3202. The indication could be the output design itself. For example, theoutput design could indicate or set each of design options for multiplefactors of the selected design case. As another example, the indicationcould indicate a design case identification (e.g., a design case number)to identify a particular selected design cases in a set or suite ofdesign cases. Output interface 3210 and output device 3206 could be oneof or comprise features of output interfaces (e.g., output interface1310) and output devices (e.g., output device 1306) described herein.For example, output device 3206 may comprise a display 3226 or a printer3228 for communicating the selected design case to a user of the system3200. Alternatively or additionally, the output interface 3210 is aninternal interface and feeds information back to the computing device3202 for further evaluation or for setting or modifying designinformation 3254.

In one or more embodiments, the system 3200 implements a method asdescribed herein (e.g., method 3300 shown in FIG. 33 for selecting adesign case).

FIG. 33 illustrates a flow diagram for outputting a selected designcase. An operation 3301 of method 3300 comprises receiving factorinformation indicating multiple factors. The multiple factors are fordesigning a system by assigning one of respective possible options toeach of the multiple factors. An operation 3302 of method 3300 comprisesreceiving range information indicating initial ranges with a respectiverange for each of the respective possible options. The initial rangesdefine a design space.

In one or more embodiments, at least one of the multiple factors is acontinuous factor that defines continuous values within a given range ofthe initial ranges. Alternatively or additionally, the multiple factorscomprise a factor with discrete options. For example, the multiplefactors comprise a categorical factor defining discrete design optionsfor the system within a category of the categorical factor. For example,the category is product material and the discrete design optionscomprise different materials (e.g., metal, plastic, paper, etc.). Asanother example of factors with discrete options, the multiple factorscould comprise a partitioned factor that is a continuous factorpartitioned into partitions using, for instance, equivalencepartitioning or taking nominal values within the continuous range. Inthese cases, a computing device, can assign level values to each of thediscrete design options. The assigned level values can be nominal valueswithin a respective initial range.

An operation 3303 of method 3300 comprises obtaining a space-fillingdesign for the design space. The space-filing design indicates selecteddesign points in the design space. The selected design points areseparated from one another in the design space based on a criterion forseparating the selected design points in the design space.

Initially, each of the selected design points represents assignedoptions assigned to the multiple factors where the assigned options areassigned from the initial ranges. In one or more embodiments, theinitial ranges are refined or updated (e.g., using the method 3300).

An operation 3304 of method 3300 comprises generating, based on thespace-filling design, an initial design suite that provides initialdesign cases corresponding to one or more of the selected design points.Each element of a respective design case of the initial design suite isone of the assigned options represented by a selected design point ofthe one or more of the selected design points.

An operation 3305 of method 3300 comprises generating evaluations of theinitial design cases by modeling respective initial responses of thesystem for each of the initial design cases. Each of the respectiveinitial responses corresponds to an operation of the system defined byeach element of a given respective initial design case of the initialdesign suite.

An operation 3306 of method 3300 optionally comprises selecting, basedon the evaluations of the design cases, a design case. An operation 3307of method 3300 comprises outputting, based on the evaluations of theinitial design cases, an indication of a selected design case.

In other embodiments, a design case is not selected or is onlytemporarily selected in order for further refinement and evaluationbefore outputting an indication of a selected design case.

An operation 3308 of method 3300 optionally comprises determiningwhether to end the operations in the evaluations period 3310 in themethod 3300. If the evaluation period 3310 is ended, an output of anindication of a selected design case is performed in operation 3707. Ifthe evaluation period 3310 is repeated, one or more ranges can beupdated from the initial range in an operation 3309 of method 3300.

For example, if the range information received in operation 3301indicates a first range of the initial ranges for an experimental factorof the multiple factors, the operation 3306 can comprise selecting,based on the evaluations of an initial design cases in operation 3305, afirst design case with a first design option for the experimentalfactor. The operation 3309 comprises determining an updated range forthe experimental factor. The updated range comprises the first designoption and the updated range is a subset of the first range.

In this way, the method 3300 can be used to explore or experiment withdesign options around a particular factor or factors of a design case.For example, the range information received in operation 3301 or in aseparate operation could be used to obtain a predefined percentage foreach respective initial range. Then operation 3309 comprises determiningthe updated range for the experimental factor by using the predefinedpercentage to determine the subset of the first range around the firstdesign option.

Operation 3309 is described as updating a range for a given factor, butan updated range can be selected for more than one or all of the factorsof the multiple factors such that each of respective updated ranges forthe factors is a subset of an initial range corresponding to the samefactor.

One of ordinary skill in the art will appreciate that these operationscould be performed in a different order than presented here withoutdeparting from the method. For example, operation 3301 could occurbefore, after or simultaneously with operation 3302. An operation 3308to determination whether to end the evaluation period 3310 could beperformed after or simultaneously with an operation 3309. In additionoperations described herein could be combined or broken intointermediate operations.

In one or more embodiments, method 3300 is used to update ranges tonarrow the possible design space in operation 3303. For example, in oneor more embodiments where a range is updated for at least one factor(e.g., an experimental factor), the operation 3303 comprises determiningan updated design space defined by the updated range for theexperimental factor. A space-filling design is then obtained that is anupdated space-filling design based on a criterion for separatingselected design points in the updated design space.

In the same or different embodiment, where a range is updated for atleast one factor (e.g., an experimental factor), the operation 3304comprises generating an updated design suite that provides updateddesign cases for the system according to the updated range or ranges.For example, the operation 3304 comprises generating the updated designsuite based on the updated space-filling design. Each element of anupdated design case corresponds to one of the multiple factors. If therange is updated for the experimental factor, one element of each of theupdated design cases corresponds to the experimental factor and isselected from the updated range for the experimental factor.

Operation 3305 can be used to generate evaluations of the updated designcases by modeling respective updated responses of the system for each ofthe updated design cases. Each of the respective updated responsescorresponds to an operation of the system defined by each element of agiven updated design case of the updated design suite.

In this case where further refinement is executed to produce updateddesign cases, the operation 3307 can be used to output the indication ofthe selected design case, based on the evaluations of the updated designcases as well. In this way, the evaluation period 3310 can be usediteratively to select a design case.

For example, operation 3308 can be used to determine how many iterationsof the evaluation period 3310 are left for refining a range of initialranges received in the range information in operation 3302. Theoperation 3309 can be used in conjunction with operation 3308 to set arefined range that is a subset of a range of a previous updated designsuite. In this way the method 3300 can be used to iterate through theevaluation period 3310 to evaluate a further updated design suite. Forexample, the evaluation period 3310 can be used to generate a furtherupdated design suite that provides design cases for the system accordingto the refined range and generate evaluations of the further updateddesign cases by modeling respective responses of the system for each ofthe further updated design cases. Each respective response correspondsto an operation of the system defined by each element of a furtherupdated design case of a further updated suite. The operation 3306 ofevaluation period 3310 can be used to select, based on the evaluationsof the further updated design cases, a design case. After each iterationof the evaluation period 3310, it can be determined whether to updatethe refined range based on the selected design case and output, based onthe updated design suite, an indication of a selected design case byoutputting an indication of a selected design from the further updateddesign cases.

The number of iterations can be determined by one or more of thefollowing ways. A computing device (e.g., computing device 3202) can setthe iterations by obtaining an indication of a number of iterations forupdating the initial design suite. For example, a user or the computingdevice may determine limitations on how much processing capabilities todevote to searching for an optimal design case. Alternatively oradditionally, the number of iterations is set based on obtaining athreshold for evaluating a response of the system defined by updatedoptions of each of the design cases of the further updated design suite.For example, there may be an evaluation of a design case that is goodenough that no further refinement is really needed, and it would saveprocessing capabilities to end the evaluation period 3310. Otherexamples of how to set the number of iterations are possible (e.g., asdescribed in more detail in the examples that follow).

In one or more embodiments, a computing device (e.g., computing device3202) is configured to display, on a display device, a graphical userinterface for user entry or modification of user information in thegraphical user interface. For example, the user information couldinclude one or more of factor information, range information; and anumber of design cases for a given design suite. The computing device,receives, from a user of the graphical user interface (e.g. via one ormore input devices) user input indicating the user information.

For example, FIGS. 34A-B illustrate an example of a graphical userinterface 3400 for controlling generation of a design suite. Thegraphical user interface 3400 is used in the context of designing asystem for a machine learning algorithm. This is merely an example, andthe graphical user interface 3400 could be used for designing othersystems. Given its application, the factors 3404 are hyperparameterswhich are parameters whose settings affect the learning algorithm. Inthis case, selecting a design case is used as part of hyperparametertuning, which involves finding an optimal set of options or values forthe hyperparameters for a given problem.

An objective 3402 is set for the machine learning algorithm. In thiscase, a regression with squared loss is selected for the learning taskobjective. This could be an objective provided by XGBoost. XGBoost is anoptimized distributed gradient boosting library that implements machinelearning algorithms under the Gradient Boosting framework. XGBoost canbe run in distributed environments (e.g., a parallel processing databaseprovided by Hadoop®).

Current or default options 3406 are assigned for each of the factors3404. For example, the maximum depth of a tree is assigned a value of 6.In one or more embodiments, these are default hyperparameter valuesstored in a computing system. For example, the window in graphical userinterface 3400 could be presented with a launch dialog for XGBoost(e.g., after selecting responses, inputs and validation column(s)).Embodiments described herein can be implemented using or integrated withdata analytics software application(s) and/or software architecture suchas software tools offered by SAS Institute Inc. of Cary, N.C., USAdescribed herein (e.g., JMP®).

In one or more embodiments, a selection can be made in the graphicaluser interface 3400 to control a computing device to find betterparameters (e.g., by checking the tuning design checkbox 3450). In theexamples shown in FIGS. 34A-34B, only the main set of hyperparametersare shown for this machine learning algorithm, but one could extend thisto any number of additional hyperparameters or apply these techniques toa different algorithm that requires a different set of hyperparameters.

Clicking the tuning design checkbox 3450 may cause the graphical userinterface 3400 to present default ranges for each of the hyperparametersspecifying the minimum and maximum under consideration. The text boxesin this graphical user interface 3400 can be editable for users tomodify. In an alternative embodiment, one or more hyperparameters may beprevented from modification (e.g., a setting controlled by a checkbox ifan administrator does not want the value to change). Similarly,restrictions can be placed on the number of levels that can be taken onby a hyperparameter. For example, there may be a maximum spread betweenthe minimum and maximum values to control the size of a design space andtherefore processing time to select a design case.

Under traditional approaches to exploring options for factors within adefined hyperparameter space, practitioners took a grid approach (i.e.checking different values within the range at regular intervals) orrandom sampling (i.e. randomly selecting values within the range). Oneor more embodiments, improve this by taking a design of experimentsapproach (e.g., by using space-filling designs to choose the options forthe hyperparameters). In this example, where the factors had continuousinput options, a fast flexible space-filling design using the MaxProcriterion can be used to generate a space-filling design.

For example, the computing device (e.g., computing device 3202) canobtain a space-filling design by mapping the design space onto a matrixwith rows and columns. The computing device can determine primaryclusters for the design space, each containing a different set ofrepresentative design points that is mutually exclusive ofrepresentative design points in other primary clusters of the designspace. The computing device can select the selected design points byselecting a representative design point from each primary cluster thatminimizes a MaxPro criterion based on:

${\min\limits_{D}}_{\psi(D)} = \left\{ {\frac{1}{\left. (_{2}^{n} \right)}{\sum\limits_{i = 1}^{n - 1}{\sum\limits_{j = {i + 1}}^{n}\frac{1}{\prod\limits_{l = 1}^{p}\left( {x_{il} - x_{jl}} \right)^{2}}}}} \right\}^{1/p}$where ψ(D) is the criterion; i, j, l are integer counters; n is aninteger number of primary clusters for the design space; p is an integernumber of continuous variables for the design space; and x_(ab) is anentry in row a and column b of the matrix.

Alternatively, other approaches could be used to determine aspace-filling design. If there are categorical factors for example, aspace-filling design can be constructed using techniques describedherein (e.g., a modified MaxPro criterion). Alternatively approaches tospace-filling designs include minimax or maximin Latin hypercube designsor designs using any other space-filling criterion besides a MaxProcriterion. In this example, a MaxPro criterion is selected due to itsbetter projection properties for lower-dimensional projections. That is,if a smaller subset of hyperparameters has a larger impact on thealgorithm, a MaxPro design does a better job of spreading out designpoints in that projection.

In one or more embodiments, a user is able to specify a minimum and amaximum of a range 3452 for one or more of the inputs. For example, asshown in FIG. 34B, the user can edit range 3452A and 3452B. These rangeswill define the design space. Additionally, or alternatively, more thanone range could be selected for a factor. For example, the range 3452Bcould include multiple ranges (e.g., a range from 1-4 and a range from8-10).

Alternatively or additionally, a computing device can receiverestriction information indicating disallowed options within a givenrespective range of the range information; and obtain the space-fillingdesign by restricting potential design points of the design space thatrepresent disallowed options in the design space. For example, adisallowed combinations option 3454 may be selected to provideinstructions to a computing device (e.g., script from a user)disallowing combinations involving two or more factors. FIG. 34C showsan example of a graphical user interface 3460 for using a slide bar 3462to input disallowed combinations within the range 3452A for factorcolsample_bytree and range 3452B for factor min_child_weight. In theexample shown in FIG. 34C, when factor colsample_bytree is assigned avalue between 0.8 and 1, factor min_child_weight is not allowed to beassigned a value between 1 and 3.

In one or more embodiments, a user is able to indicate a number ofdesign cases to generate for a design suite. In this example, thegraphical user interface 3400 comprises an editable run field 3456 for auser to select the number of design cases.

A button can be selected to indicate the user is ready to generate adesign. In this case when the user hits the “Go” button 3458, acomputing device generates a design with the specified number of runs(i.e. the number of combinations of hyperparameters to evaluate) in therun field 3456. For example, when the tuning design checkbox 3450 ischecked, the “Go” button initiates the creation of a design suite anduses the hyperparameter settings from the design cases of the designsuite to invoke XGBoost for each run or design case of the design suite.

FIG. 35 illustrates a display 3550 with an example of a portion of adesign suite 3500 generated according to one or more techniquesdescribed herein. Each row of the design suite 3500 represents differentdesign cases with different options selected for each of the factors.For this example, only 17 cases are shown of the 20 cases selected inthe run field 3456. Further, for simplicity, the selected options areshown as rounded values. Each of the design cases have been modeled fora response of the machine learning algorithm according to the optionsprovided in the design suite 3500. The design cases can be modeled usingone or more modeling techniques including gradient boosted tree, aGaussian process, or a neural network and evaluations 3502 provided foreach of the models. Other modeling techniques can be used (e.g.,provided by JMP® and XGBoost). A response, such as validation R-square,is used to evaluate the model.

FIG. 36 illustrates an example of a graphical user interface 3600 forcontrolling an indication of a selected design case. A portion of designsuite 3500 is shown with less rounding of options compared to thedisplay 3550. One or more embodiments provide a graphical user interface3600 with space-filling design control 3602 to allow control of thespace-filling design (e.g. to select how the space-filing design isgenerated). Alternatively or additionally a computing device can selector provide a default space-filling design technique.

In the same or different embodiments, the graphical user interface 3600provides a factor control 3604 to control the factors implemented in themodeling (e.g., to exclude factors that the user is less concerned aboutimpacting the response). In the same or different embodiments, thegraphical user interface 3600 provides a design case control 3606 tocontrol the design cases implemented in the design suite (e.g., toexclude design cases that a user anticipates will not produce desirableresponses or select design cases that a user anticipates will producedesirable responses).

In one or more embodiments, one or more design cases can be selectedfrom the design suite 3500 based on the evaluation of the responses.

In the same or different embodiments, a computing device (e.g.,computing device 3202) determines, based on the evaluations of theinitial design cases, the first design case by generating a generateddesign case different than all of the design cases of the design suite.For instance, the design suite can be used as a starting point foranother hyperparameter optimization technique such as AUTOTUNE.

FIG. 37 illustrates an example of model results predicted, in aprediction profiler 3700 from XGBoost, for individual factors of designsuites based on model responses of the design suite. As shown in FIG. 37, design options for a new design case different than the design casesof the design suite is selected that maximize a desirability of theresponse, plotted in graph 3706.

For instance, a correlation of individual design options and theevaluations of the initial design cases is determined to help selectdesign case(s). For instance, as shown in graph 3702 at higher valuesfor the factor colsample_bytree there is a correlation with a predictedhigher response value. In graph 3704, lower values of factormin_child_weight show a correlation with a predicted higher responsevalues. Therefore, a design case may be generated that has higher valuesfor factor colsample_bytree and lower values for min_child_weight.

In the same or different embodiment, it may be useful to select morethan one design case. For example, as shown in graph 3702, there arehigh predicted response values for values at the edges of the range ofoptions for the factor alpha. In this case it might be useful to select,based on the evaluations of the design cases, a subset of the initialdesign cases or multiple generated design cases (e.g., generated fromthis subset). Design suites can be generated for each of the selecteddesign cases for evaluating respective responses of the system definedby updated options of each of the generated design suites. A design casecan be selected or generated from one of the different design suitesbased on an evaluation of one or more of the design suites. This may beuseful for exploring different design points or aspects of a range inparallel (e.g., different edges of a range).

FIGS. 38A-B illustrates an example of model results or prediction formultiple iterations of evaluating design suites.

While the original space-filling design is intended to search over thelarger hyperparameter space, as shown in FIGS. 38A-38B, a computingdevice can use the best hyperparameter settings from the original designto construct another space-filling design over a narrower region. Thisleads to an iterative process, where one would narrow the range for eachparameter for each iteration (e.g. until there is no furtherimprovement). While this may be time consuming if the model is expensiveto fit, for cases where multiple iterations are feasible, one could letthe iterative process continue for the available time.

FIG. 38A shows a first model at point X in location 3802 based on thedefault set of values for designing the system. FIG. 38B shows a portionof the corresponding responses 3852 for each of the models in agraphical user interface 3850 for comparing model evaluations. Fiftymodels (models 2-51) were generated based on model 1 for further testingusing a space-filling design. The evaluations for these models areplotted in FIG. 38A. As shown in FIG. 38B, the best model from this setis model 12 and is plotted at a point in location 3804 in FIG. 38A.

Model 52 is generated from using XGBoost on Val Rsquare from Models1-51. A combination of design options for all elements of this designcase corresponding to Model 52 are different from any combination ofdesign options for elements of the design cases of the initial designsuite. Model 52 is plotted at point Y in location 3806 and results in aslightly better evaluation than model 12, so it is selected as astarting point for generating a new design suite for models 53-102roughly centered about Model 52 (ad hoc).

Model 103 is generated from using XGBoost on Models 1-102. Model 103 isplotted at point Z in location 3808. A different range of models couldbe used to generate models 52 and 103 (e.g. only models 52-102 togenerate model 103 or model results could be ad hoc excluded by a user).

Accordingly, FIG. 38A plots the model results for an interactive processwith the following steps: (1) fit the original default hyperparameters;(2) use a space-filling design with the default ranges; (3) use XGBoost(or some other modeling technique) with the desired objective functionbased on the hyperparameters and maximize desirability with the profiler(or other optimization operation); (4) Use a space-filling designcentered around the best hyperparameter settings from step 3 but withsmaller ranges; (5) repeat steps 3 and 4 for a specified number oftimes, or until no improvement is found to the objective function.

In one or more embodiments, there are further possible refinements ofthis approach. The ranges in (4) can be dictated by importance metricsderived by the modeling. Other possibilities are to add additionalvalidation columns in additional iterations through (5) or using (4)centered around a few different points that look promising, and toconsider more than one type of evaluation for each model (e.g.,different metrics).

From FIG. 38A, the original tuning table or design suite only has onepoint that is close to the default hyperparameter settings. Thepredicted optimal model 52 ends up being better than anything observedin the set of models 2-51. In the second tuning table or design suite(models 53-102), many of the observed models are better than the defaultmodel in that they resulted in higher evaluations of value R Square (ValRSquare). By performing a second iteration, an improved region foroptions is found. However, the further iteration in this case yielded nosignificant improvement for this example.

In one or more embodiments, a computing device can compute a comparisonresult by comparing the evaluation of a design case (e.g. model 103) toone or more models of evaluations of previous design cases (e.g., modelsin the set 1-102). The number of iterations of generating design suitesand evaluations can be set based on the comparison result. In this case,no further iterations are likely to produce better results given thenumber of observed models that resulted in better or roughly equalevaluations as a generated design case. Additionally, or alternatively,a graphical user interface can display an improved region for optionsfor a user to decide whether to perform another iteration of refinementof the ranges for selecting a design case.

FIG. 39 illustrates an example of the graphical user interface 3400 forcontrolling generation of a design suite. Ranges 3452 have been updatedand narrowed as a result of previous tuning. For example, range 3452Ashows narrowing around the upper range shown in FIG. 34B, and range3452B shows narrowing around the lower range shown in FIG. 34B. Thiscomports with the improvements shown in the correlations of FIG. 37 .

The user can select runs in run field 3456 for the amount of designcases to generate. In this example, the user has entered a greateramount of runs in run field 3456 than the original iteration shown inFIG. 34B, but the user could have entered less runs or the same numberof runs.

Using one or more devices or processes described herein a user canexplore an optimal design case for designing a system.

Another goal of a test engineer is to determine if an algorithm orsoftware meets its stated requirements or does what the tester expectsit to do (e.g., a configuration validation for a machine learningalgorithm). Producers of software also want to validate software touncover issues with the software before users or customers do. Qualitystatements about software from manual testing may be incomplete, and auser does not know if the software has been tested for their typical usecases, the type of data they have, and their measure of expectation fromthe software. A user may even do their own manual testing beforesoftware can be distributed to their scientists and engineers.

Complex software systems (e.g., XGBoost a widely used open sourcegradient boosting library supported in software platforms by SASInstitute Inc. of Cary, N.C.) may have components or algorithms in thesoftware system developed independently by several different developmentgroups and then integrated together (e.g., into a library). Althoughimplementation of the individual algorithms may have been rigorouslytested by individual development groups, it may be infeasible tovalidate an integrated system as rigorously. Furthermore, the softwaresystem may provide a set of parameters (e.g., hyperparameters) thatallows users to configure a particular algorithm at run time. It mayinfeasible to rigorously test the configuration space. XGBoost offersthirty-four hyperparameters, some of which are continuous values and sothe configuration validation space is infinitely large.

One or more embodiments provide a more reliable, efficient, and easiermethod for algorithm or software validation by finding any deviationsfrom an expected result without having to test every possible input.

FIG. 40 illustrates an example block diagram of a validation system 4000for locating deviation from a specified result of a validationspecification. For instance, a test engineer testing a system may notwant or expect the tested system to stop operating in response to aninput. The validation specification for the tested system in operationcould be a validation that the tested system will not stop operating.The validation system 4000 includes a computing device 4002 for locatingany deviation (e.g., any inputs that would cause a system crash). In oneor more embodiments, the computing device 4002 is the same or differentfrom computing devices described herein (e.g., computing device 1302 orcomputing device 3202). For instance, a test engineer may want toexplore a design space to find optimal settings using techniquesdescribed herein, and also want to look at edge cases for the inputsthat may be problematic (e.g., that cause a crash, processing delays, orpoor outputs). The test engineer may want to test for combinations ofinputs that induce failures in this regard.

The validation system 4000 is configured to exchange information betweendevices (e.g., one or more input devices 4004 and/or one or more outputdevices 4006) in the validation system 4000 (e.g., via wired and/orwireless transmission) and/or devices in other systems described herein(e.g., system 1300 and system 3200). For example, a network (not shown)can connect one or more devices of validation system 4000 to one or moreother devices of validation system 4000. In one or more embodiments,fewer, different, and/or additional components than shown can beincorporated into validation system 4000 (e.g., components of system1300 and/or system 3200).

In one or more embodiments, the computing device 4002 receives designinformation 4050 related to a system of operation (e.g., for designing asystem or operating a designed system described herein and not shown inFIG. 40 ). For example, in one or more embodiments, the designinformation 4050 comprises factor information 4052 indicating one ormore factors for the system. The system of operation can operateaccording to a respective input of respective candidate inputs for eachfactor of the one or more factors. For instance, the system of operationcould be a model receiving inputs or a physical system with componentsor settings according to the inputs. In one example explained herein,the system in operation could be a system employing an algorithm withvalues assigned for the factors affecting how the algorithm isstructured or used. One example of an algorithm is a machine learningalgorithm where hyperparameters control operation of the machinelearning system. The user designing the system many need to determinewhat settings for these hyperparameters may lead to a delay inprocessing in that system. Design information 4050 could comprise otherdesign information described herein (e.g., design information 3254).Alternatively, or additionally, design information 4050 comprises otherinformation related to design objectives, requirements, or constraints(e.g., a number of test runs).

Alternatively, or additionally, the computing device receives a request4060 to validate a response of the system of operation. For instance,the request 4060 may be according to a validation specification 4062(e.g., the software crashing, length of time to run a system,questionable system results, etc.). The validation specification 4062may be for the initial design space. For instance, the validationspecification 4062 may be for validating all of an initial set of designpoints for the system of operation according to the respective candidateinputs for each factor of the multiple factors. The validationspecification may relate to determining deviation from a specifiedresult in response to testing one or more candidate inputs of the designspace for each factor of the multiple factors (e.g., exceeding athreshold or a percentage increase). The specified result may be anoracle or expected result.

The computing device 4002 could receive information or requests from oneor more input devices 4004 (e.g., mouse 4020, keyboard 4022, computingsystem 4024 or other input devices described herein). For instance, auser may use the one or more input devices 4004 to enter or select arequest or validation specification type. Alternatively, oradditionally, computing device 4002 may generate the information orrequests (e.g., based on defaults stored in computer-readable medium4012). For instance, JMP® provided by SAS Institute Inc. of Cary, N.C.can generate a list of hyperparameters and possible input values, butalso allow users to add additional inputs or factors.

In one or more embodiments, the computing device 4002 receivesinformation or requests via one or more input interface(s) 4008. Theinput interface(s) 4008 could comprise one or more features of an inputinterface described herein or is an input interface described herein(e.g., input interface 1308 or input interface 3208). For instance, theinput interface(s) 4008 could receive input received from an externaldevice or internally within computing device 4002.

In one or more embodiments, the computer-readable medium 4012 comprisesone or more features of one or more computer-readable mediums describedherein or is one of computer-readable mediums described herein (e.g.,computer-readable medium 3212 or computer-readable medium 1312). In oneor more embodiments, the computing device 4002 has a processor 4014. Forinstance, the processor 4014 comprises one or more features of one ormore processors described herein or is one of processors describedherein (e.g., processor 1314 or processor 3214).

In one or more embodiments, computer-readable medium 4012 storesinstructions for execution by processor 4014. For example, in one ormore embodiments, the computer-readable medium 4012 comprises a testsuite application 4040 for generating a test suite that provides testcases for testing the system of operation. Each test case of the testsuite comprises test conditions for each factor of the multiple factors.Each test condition corresponds to a given one of the respectivecandidate inputs for each factor of the multiple factors. The test suiteapplication 4040 may determine an initial design space for generatingthe test suite. For instance, the initial design space may define aninitial set of design points for the system of operation according torespective candidate inputs for each factor. Alternatively, oradditionally, the test suite application 4040 may determine a subsetdesign space that defines a subset of the initial set of design pointsbased on constraints on the initial design space (e.g., received designconstraints like disallowed combinations). The test suite application4040 may generate this test suite in a strategic way so that not everyinput needs to be tested. For instance, the test suite application 4040may generate data representing a covering array of strength t with allcombinations, for design points defined by the initial design space orthe subset design space, involving t factors of the multiple factors.Alternatively, or additionally, the data may comprise test cases basedon other types of testing protocols (e.g., combinatorial,pseudo-exhaustive, random, or adaptive random approach). Alternatively,or additionally, the data may comprise test cases from previous tests oruser-defined test cases.

Alternatively, or additionally, the computer-readable medium 4012comprises a deviation application 4042 for locating a deviation from aspecified result of the validation specification. The deviationapplication 4042 may validate results for hyperparameters inputs givenby a covering array generated by the test suite application. Forinstance, the deviation application 4042 may comprises instructions forobtaining a response 4044 of the system of operation according to one ormore test cases of the test suite. For instance, a test engineer maytest a physical system and input the responses using input device(s)4004. Alternatively, or additionally, the test suite application 4040may obtain or generate a computer model for generating a response of thesystem of operation according to a test case of the test suite. Thecomputer-readable medium 4012 obtains the responses 4044 of the systemof operation based on the test suite application 4040 generating theresponse according to the computer model. The responses 4044 couldinclude for instance a failure region in the design space or statisticsfor the design. For example, a user might want to keep track ofexecution time, and declare that if any set of factors causes analgorithm to run for more than 300 seconds, then that set of factors hasprecipitated a failure in the algorithm. Alternatively, or additionally,the deviation application 4042 may comprises instructions for generatingan output indicating a deviation from the specified result (e.g., a timeover 300 seconds and/or the factors that lead to the deviation).

In one or more embodiments, the test suite application 4040 or deviationapplication 4042 may be performed in response to the request 4060 tovalidate the response of the system of operation (e.g., to return aresponse to a user requesting the validation).

In one or more embodiments, the computing device 4002 outputs to anoutput device 4006 a deviation 4070. This could be indicated forinstance by a design case indication 4072 indicating the design or testcase that precipitated the deviation (e.g., a set of each design optionfor factors of a test case, or a design case identification as describedherein). Output interface 4010 and output device 4006 could be one of orcomprise features of output interfaces (e.g., output interface 3210and/or output interface 1310) and output devices (e.g., output device1306 and output device 3206 described herein). For example, outputdevice 4006 may comprise a display 4026 or a printer 4028 forcommunicating the deviation to a user of the validation system 4000.Alternatively, or additionally, the output interface 4010 is an internalinterface and feeds information back to the computing device 4002 forfurther evaluation or for setting or modifying design information 4050.

In one or more embodiments, the validation system 4000 implements amethod as described herein (e.g., methods shown in FIGS. 41A-B).

FIG. 41A illustrates an example flow diagram of a method 4100 forreceiving a request to validate according to a validation specification.The method could be implemented by one or more devices of validationsystem 4000 (e.g., computing device 4002). Example embodiments (e.g.,for devices, systems or methods described herein) will be described inthe context of indicating a deviation from a specified result forhyperparameters of a machine learning algorithm. Those of ordinary skillin the art will appreciate other applications for embodiments describedherein.

An operation 4101 of the method 4100 comprises obtaining multiplefactors of a system of operation that is to operate according to arespective input of respective candidate inputs for each factor of themultiple factors. In one or more embodiments, the computing systemreceives one or more custom factors. Alternatively, or additionally, thefactors may be default factors for the system of operation.

An operation 4102 of the method 4100 comprises receiving a request tovalidate, according to a validation specification for the initial designspace, a response of the system of operation. The validationspecification relates to determining deviation from a specified resultin response to testing one or more candidate inputs of the initialdesign space for each factor of the multiple factors.

FIG. 42 illustrates an example graphical user interface 4200 forvalidating a system of operation according to a validation specificationfor a machine learning algorithm. The graphical user interface 4200 inthis example has a checkbox 4206 for tuning the design as described inembodiments herein. With hyperparameter tuning, a design space isexplored to find good settings for some objective for the algorithm.Embodiments herein also offer additionally, or alternatively validationoptions 4204 (e.g., as part of system options 4202 such as systemoptions of XGBoost provided by SAS Institute Inc. of Cary, N.C.).Validation is different than hyperparameter tuning in that edge casesare explored for inputs that may be problematic. A purpose of validationtesting is to test for combinations of inputs that induce deviation froma validation specification (e.g., failures). This can be particularlydifficult in situations where there are many factors or infinite inputsfor those factors (like hyperparameters which can include continuousfactors or “Advanced Options” factors).

An operation 4103 of the method 4100 comprises determining an initialdesign space that defines an initial set of design points for the systemof operation according to the respective candidate inputs for eachfactor of the multiple factors, or determine a subset design space thatdefines a subset of the initial set of design points based onconstraints on the initial design space.

An operation 4104 of the method 4100 comprises one or more operationsresponsive to the request to validate the response of the system ofoperation. For instance, one or more operations of a method 4150 in FIG.41B for locating deviation from a specified result of a validationspecification. For instance, method 4150 could be used to find a valuefor a hyperparameter that would cause the machine learning algorithm totake too long to execute.

An operation 4151 of the method 4150 comprises generating datarepresenting a covering array of strength t with all combinationsinvolving t factors of the multiple factors of the covering array fordesign points defined by the initial design space or the subset designspace. An operation 4152 of the method 4150 comprises generating, basedon the data, a test suite that provides test cases for testing thesystem of operation. Each test case of the test suite comprises testconditions for each factor of the multiple factors. Each test conditioncorresponds to a given one of the respective candidate inputs for eachfactor of the multiple factors. For instance, the test suite couldcomprise all or some test cases corresponding to a covering arraygenerated according to operation 4151. Alternatively, or additionally,the test suite comprises additional test cases or test cases derivedfrom testing according to a test suite. The economic run size ofcovering arrays lends itself to being an efficient tool that may be usedto validate in even situations where there are large numbers of factorsor inputs (e.g., the hyperparameter space of a machine learningalgorithm).

An operation 4153 of the method 4150 comprises obtaining the response ofthe system of operation according to one or more test cases of the testsuite. For instance, the obtaining comprises generating the responses(e.g., from a model of the system of operation) or receiving theresponses (e.g., from testing a physical system).

An operation 4154 of the method 4150 comprises generating an outputindicating a deviation from the specified result.

FIGS. 43A-E illustrate an example test suite for validating a system ofoperation according to a validation specification (e.g., in response toa request to validate via the graphical user interface 4200 in FIG. 42). As shown, some of the factors are continuous factors which is treateddifferently than categorical because continuous factors have either aninfinite range, or many more values than a tester would be expected (orwant) to test. As shown in the graphical user interface 4300 in FIG.43A, a computing system (e.g., one or more devices of the validationsystem 4000) responsive to the request to validate the response of thesystem of operation (e.g., a machine learning algorithm) can generate afirst set of candidate inputs for the first factor by discretizing acontinuous range of candidate inputs for a factor into discrete optionswithin the continuous range of candidate inputs for the factor. Forinstance, max_depth factor has a role of continuous in the graphicaluser interface 4300, but has been discretized into values 3 or 9. Theinitial design space can be restricted by constraints related to thediscrete options.

Continuous factors can be discretized by applying, for instance,equivalence partitioning to partition the range of such factors or othertechniques described herein. The continuous factors can be automaticallypartitioned by the computing system or augmented in response to userinstructions. FIG. 43E shows the roles of the partitioned continuousfactors changed to categorical with specified input values in thegraphical user interface 4380.

FIGS. 43B-D demonstrate a test suite for validating the factors ingraphical user interface 4300 of FIG. 43A. In this case, a coveringarray was used of strength t equal to 2. A covering array of strength tcomprises all combinations involving t factors of the multiple factors.The design space for the covering array was already constrained by, forinstance, lower and upper bounds for continuous factors. In this case,the design space for the covering array has been further restricted byconstraints according to in the discretized factors in FIG. 43E.

The computing system responsive to a request to validate a response ofthe system, discretized the continuous range of candidate inputs for agiven factor into a set of candidate inputs with discrete options withinthe continuous range of candidate inputs for the factor and the testsuite. The test suite shown in FIGS. 43B-D comprises one or more inputsfrom the first set of candidate inputs. For instance, max_depth is acontinuous factor as shown in FIG. 43A and discrete values are given forthis value of 3 or 9. The resulting test suite in FIGS. 43B-D showvalues of 3 or 9 assigned in the test suite.

The generated test suite (e.g., the covering array in FIGS. 43B-D) couldautomatically execute testing according to the covering array (e.g.,executing an XGBoost platform for each run of the covering array).Alternatively, the test suite could be provided as a data table, with ascript, that could be used to invoke testing when the user chooses toexecute the script (e.g., by executing the XGBoost platform for each rowof the data table). The script technique is useful if the user wants toadd some additional tests or make modifications to the test suite.Alternatively, the test suite could be provided as a data table formanually testing a machine and receiving inputs for tests according tothe test suite.

In this example, the strength of the test suite was only 2 to provide acompact test suite, but higher strength test suites can be conducted.For instance, JMP® software provided by SAS Institute Inc of Cary, N.C.can construct covering arrays of strength 6 or full factorial coveringarrays. Covering arrays are a useful underlying construct used forcombinatorial testing. They have the property that, for any subset of tfactors, all combinations of settings for the factors occur at leastonce. Software failures, from a wide variety of domains, are often dueto the combination of relatively few factors and so combinatorialtesting is a highly efficient and effective way to validate softwaresystems.

As stated by Boris Beizer colloquially, “Bugs lurk in corners andcongregate at boundaries.” The boundaries that Beizer refers to reflectsoftware failures induced by edge cases; whereas the corners are thevalues of two or more inputs that induce a failure. By using coveringarrays, one or more embodiments efficiently look at the “corners” of theinputs by looking at the interactions between inputs. The boundaries forcontinuous factors can occur due to logical statements in the underlyingcode.

A tester may have specialized knowledge that a particular factor islikely to lead to a deviation (e.g., based on testing other systems). Inone or more embodiments, graphical user interface 4300 can be used todenote or receive an indication of prioritized factors. Prioritizedfactors can be used to generate a test suite focused on theseprioritized factors. For instance, the test suite may be generated witha variable strength covering array whereby portions of the variablestrength covering array involving one or more prioritized factors have agreater strength than other factors of the multiple factors in the testsuite.

FIG. 44 illustrates an example graphical user interface 4400 fordisplaying a response of validating a system of operation according to avalidation specification. In this example, the validation specificationrelates to inputs that would cause a failure, and only one test caseresulted in a failure (or response 0).

In one or more embodiments, a computing system generates a set ofpotential causes for each failure mode based on the test suite. FIG. 45illustrates an example graphical user interface 4500 for displayingpotential causes in response to a single test case failure.

Potential causes can be tested. For example, causes for failures in thecontext of hyperparameters can be tested by running the machine learningalgorithm varying only the potential causes. Multiple causes can betested at a time (e.g., by creating tests that attempt to halve thepotential causes). This approach is easier when there is a set ofbaseline factors that is known to pass (e.g., known hyperparameters orinputs that passed testing). Options 4502 can be used for furthertesting of the potential causes.

In some cases, multiple responses of the system deviate from thespecified result. For instance, FIGS. 46A-B illustrate example graphicaluser interfaces for displaying potential causes in response to multipletest case failures. In FIG. 46A, the graphical user interface 4600 showsmultiple responses that had a value of 0 indicating a failure.

In this case, the computing system can determine commonalities betweentest cases that resulted in multiple responses of the system deviatingfrom a specified result. The computing system can generate, based on thecommonalities, multiple cause indicators. Each cause indicator of themultiple cause indicators represents a likelihood that a test conditionor combination of test conditions caused the deviation from thespecified result. The computing system can generate an output indicatingthe deviation by outputting an indication of a most likely potentialcause based on the multiple cause indicators.

FIG. 46B shows an ordered ranking of one or more potential causes forfurther testing (e.g., based on cause indicators). In this example, eachpotential cause of the one or more potential causes comprises one ormore factors of the multiple factors with assigned input according to atest case of the test suite that resulted in a response that deviatedfrom the specified result. The ranking can be based on for instancecommonalities between test cases (e.g., based on failure count).

Additionally, or alternatively, the ranking can be based on otheroptions 4502 (e.g., factor, input or combination weights).

FIGS. 47A-B illustrate example graphical user interfaces for displayingpotential causes ranked based on weights and commonalities.

In FIG. 47A graphical user interface 4700 can be used for a computingsystem to receive an indication of one or more prioritized factors inthe multiple factors (e.g., prioritized factor has a factor weight4702). These weights can be received from a user. For instance, a testermay have specialized knowledge that a particular factor is likely tolead to a deviation or could be generated by the computing system (e.g.,based on testing other systems). In one or more embodiments, theprioritized factors can be weighted (e.g., given a factor weight of 8 inthis example) and potential causes involving this factor can be promotedin ranking for testing.

The computing system can receive multiple weights (e.g., in addition toor alternatively to a factor weight). For instance, as shown thecomputing system can receive an input weight 4704 for a candidate input.In this case, test cases with values greater than 0.5 for the factorsub_sample are given a weight of 2 in this example. Test cases orpotential causes involving these inputs can be prioritized.

A combinatorial weight 4706 can be used to account for a combination ofcandidate inputs for respective factors of the multiple factors. Forinstance, test cases with a value of 10 for factor min_child and anassigned value of 2 for factor lambda are given a weight of 4 in thisexamples.

Other weights or multiple types of weights could be used.

In one or more embodiments, a computing system generates, based on themultiple weights, multiple cause indicators, wherein each causeindicator of the multiple cause indicators represents a likelihood thata test condition or combination of test conditions caused the deviationfrom the specified result; and generates the output indicating thedeviation by outputting an indication of a most likely potential causebased on the multiple cause indicators.

For instance, as shown in FIG. 47B, the graphical interface 4750 showsupdated most likely potential causes in accordance with the weights. Acause 4752 related to factors colsample_bytree and max_delta step hasbeen promoted to the third most likely cause based on the weightingcompared to the ranking in FIG. 46B.

In one or more embodiments, more testing can be performed based on thepotential causes (e.g., the computing system can generate an updatedtest suite based on the one or more potential causes; and obtain aresponse of the system of operation according to one or more test casesof the updated test suite).

For instance, one method for further testing would be a “binary-type”search where the potential causes are split in 2 each time. In a firststep, a minimal set of new test cases is generated. The new casesincorporate all potential causes where the potential causes are splitevenly among the new test cases. That is, if there are 12 potentialcauses, 2 test cases are generated that contain 6 potential causes each.The new set is run for testing, removing from the set of potentialcauses any in those test cases that pass. This process can be repeateduntil only one potential cause remains. A tester may have some furtherintuition for likely potential causes for reducing testing. One ofordinary skill in the art will appreciate other protocols for testingonce potential causes are identified and/or ranked as shown herein.

FIGS. 48A-B illustrate example graphical user interfaces for editing atest suite (e.g., a test suite or an updated test suite).

In FIG. 48A, the validate options 4204 shown in FIG. 42 in graphicaluser interface 4200 could include an option to run validation 4800(e.g., to generate a test suite). Alternatively, or additionally, therun validation 4800 comprises an edit test suite option 4804 for editinga test suite.

For instance, FIG. 48B shows an example graphical user interface 4850for editing a test suite. For instance, as shown the computing system,can display the graphical user interface 4850 for adding or removingfactors for the test suite. In this case, the factors comprise thehyperparameters and the user is using the add factor option 4852 to addan additional factor (K-folds factor 4854). The add factor option 4852can have options (e.g., in a drop down) for selecting the role of thefactor (e.g., categorical, or continuous) or for augmenting the numberof inputs (e.g., adding more inputs for continuous factors). In thiscase, the user has selected categorical and has specified four discretecandidate inputs 4860 for the K-folds factor 4854 (none, 5, 10, 20).

Adding additional factors, test cases or test suites can be useful foraccounting for nondeterministic results (e.g., accounting for blockingfactors). In a deterministic system, a test case that induces a failurewill consistently do so. However, other factors may lead tonon-deterministic results (e.g., different test engineers conducting theexperiment). These type of factors may be blocking factors that do notdefine components of the system of operation under test but may stillhave an effect on the outcome of an experiment for that system inoperation. Some examples of blocking factors would be different testengineers, days of the week of testing, or different computersperforming the testing. The impact of blocking factors can varyindependently of each other or be related (e.g., test engineer 1 hasmore failures on Tuesdays because she plays basketball on Mondaynights). One or more embodiments make the blocking factors obvious to auser and allow the option of producing test suites for each level of theblocking factor. This is useful for blocks such as test engineers as itwould be beneficial to send a test suite representing the test suitespecific to each test engineer. For instance, an aggregated test suitecould be of strength t+1 and each test engineer could get a test suiteof strength t. Sending a test suite allows for more efficient testingand aids in fault localization.

The computing system can generate the test suite to include one or moreblocking factors to determine if they have an impact on the response ofthe system of operation. In this example blocking factor, K-folds areused as an example. Other or additional factors could have been added asblocking factors to the test suite. K-folds can be used in differenttest regimes such as for testing a machine learning algorithm byassigning different training data sets to generate the machine learningalgorithm according to the hyperparameter factors and using a hold-outfrom the training data set to test the machine learning algorithm. Thistesting could be performed multiple times in an experiment as indicatedby the discrete candidate inputs 4860.

A remove option 4856 can be used to remove a factor (e.g., one notexpected to have an effect on the outcome of testing). Counters can beused to keep track of added and removed factors. For instance, a counter4864 can be used to keep track of the added factors. The adding andremoving of factors can be performed in other ways (e.g., using XGBoostor a special jsl command).

Alternatively, or additionally, the user can specify a number of testcases generated (e.g., in test suites option 4862). A user may wantdifferent test cases (e.g., to give to different test engineers).

In one or more embodiments, the user specifies the strength of agenerated covering array (e.g., in a strengths option 4858).Alternatively, or additionally, the user could influence the strengthindirectly such as by providing other information like a number of testcases. The computing system can set the strength based on userindicators (e.g., the strength t is a highest strength for the designspace and the total number of allowed test cases for the test suite).

As shown in FIG. 48A, the user in one or more embodiments can edit otheraspects of the computing system (e.g., the validation specification(s)option 4802 can be used to provide or view one or more validationspecifications or the disallowed combinations options 4806 can be usedto provide or view one or more disallowed combination for a design spaceof the test suite).

When using test suites designed from covering arrays, continuous factorsare discretized to allow the covering array to be constructed. Othertechniques for constructing covering arrays only allowed for categoricalfactors and required the user to manually convert continuous factorsinto categorical factors and could not account for disallowedcombinations. With disallowed combinations, a first set of values arerestricted from being assigned to the first factor from a first set ofcandidate inputs for the first factor, if a second factor is assignedone of a second set of values from the second set of candidate inputsfor the second factor. For instance, certain sets of values for thesefactors may make a model too complex or time-consuming to generate.Values may be assigned to one factor without assigning values to anotherfactor (e.g., to constrain a process metric for the system ofoperation).

Disallowed combinations are also used for categorical factors. Forinstance, options for one factor (e.g., a continuous or categoricalfactor) may be inapplicable for the system of operation if a particularvalue is selected for another factor. If disallowed combinations areneeded, in other techniques, the user would manually transform anydisallowed combination clause that involves a continuous factor to thecategorical factor space.

One or more embodiments, improve upon this technique by accounting forsubclauses of the disallowed combination expression that the continuousfactor participates in. For example, consider a covering array whichinvolves X1, X2, and X3 factors. In this example, X1 is a level factor,X2 and X3 are continuous, and the following disallowed combination isprovided:

X1==“L2” && (X2>=0 && X2<=1)∥X2>=−0.5 && X2<=0.5 && (X3>=0.5 && X3<=1).

In this example, X2 participates in the subclauses: X1==“L2” && (X2>=0&& X2<=1) and X2>=−0.5 && X2<=0.5 && (X3>=0.5 && X3<=1). Since X2participates in two subclauses, the computing system accounts for theboundaries from each subclause that X2 is involved in and so theboundaries −0.5, 0, 0.5, 1 define the partitions for X2. The factors ofthe covering array then become:

X1: 2 levels (i.e. “L1” and “L2”)

X2: 4 levels (i.e. [−1, −0.5), [−0.5, 0), [0, 0.5), [0.5, 1], whichbecome “L1”, “L2”, “L3” and “L4”)

X3: 2 levels (i.e. [−1, 0), [0, 1], which become “L1” and “L2”)

The disallowed combination for Covering Array in categorical factorspace then becomes:

X1==“L2” && X2==“L3”∥X1==“L2” && X2==“L4”∥X2==“L2” && X3==“L1”∥X2==“L2”&& X3<=“L2”∥X2==“L3” && X3 “L1”∥X2==“L3” && X3<=“L2”.

In one or more embodiments, the complexity of the disallowed combinationmay prevent generating the covering array of a specified strength t(e.g., a default or user defined strength 4858 in FIG. 48B). Forinstance, there may be missing values in the covering array due totesting so many different options in a continuous range. In this casethe computing system can be used to advantageously modify a givendisallowed combination of the one or more disallowed combinations togenerate the test suite to comprise the covering array of the strengtht.

FIGS. 49A-C illustrate example graphical user interfaces for providingdisallowed combinations in the context of an example involvinghyperparameters.

In pseudo-code form a set of actions for a factor such as ahyperparameter can be expressed as:

  If(Hyperparameter1 == a){ do thing1} Else if(Hyperparameter1<a){ dothing2} Else if(Hyperparameter1>a){ do thing3}

Hyperparameters could be continuous and/or categorical in nature as inthe above example. FIG. 49A shows in a graphical user interface 4900 adisallowed combination that is categorical in nature and one that iscontinuous in nature.

In a first scenario, the disallowed combination is categorical innature. If the “booster” is at level “dart” (with other levels“gblinear” and “gbtree”, then the input “sample_type” is not relevant.FIG. 49A shows a script for a disallowed combination for this scenariowhere the booster factor is not assigned or does not equal (!=) “dart”and & sample_type factor is assigned or equals (==) “weighted” or (∥)“uniform”.

In one or more embodiments, the computing system may simplify thesecategorical disallowed combinations for determining the design space.For instance, as shown in FIG. 49C, the scenario is reduced to a simplerform for processing by the computing system:

(booster == ″gblinear″ & sample_type==″weighted″) | (booster ==″gblinear″ & sample_type==″uniform″) | (booster == ″gbtree″ &sample_type==″weighted″) | (booster == ″gbtree″ &sample_type==″uniform″)

In a second scenario, the disallowed combination is continuous innature. The factor max_depth is continuous. It can take values of 0 toinfinity, but in this example was bounded to a range of 0 to 20 with adefault of 6. When max_depth has a value of 0, grow_policy factor cannottake the category of “depthwise”. When max_depth factor is assigned alarge value, it makes the model more complex and time-consuming, soother factors may be used to limit processing iterations.

For lambda, increasing the value will make the model more conservative,but a practitioner would not want the model to be too conservative whenmax_depth is small.

A plausible disallowed combination taking this into account thesedesigns considerations is shown in FIG. 49A:

(Max_depth==0 & grow_policy==“depthwise”)|(Max_depth>=8 &iterations>200)|(max_depth<5 & lambda>0.5).

In one or more embodiments, a computing system discretizes a continuousfactor to have discrete options by determining a boundary betweenrestricted and allowed values. The restricted values may be a first setof values that are disallowed according to the disallowed combinationand are part of the candidate inputs for that factor in a system ofoperation. For instance, the determining boundaries of the first set ofvalues may comprise the computing system extracting all indicated valuespertaining to the continuous factor from the one or more disallowedcombinations (e.g., 0, 5, and 8).

FIG. 49B shows in a graphical user interface 4930 levels assigned for a4-level categorical scheme for the continuous factor max_depth that hasbeen discretized into integer values:

L1: [0]

L2: [1, 5)

L3: [5, 8)

L4: [8, 20]

In this example, the computing system determined multiple value bins,each corresponding to a level, based on a total number of determinedboundaries of the first set of values. In this case, there were 3 valuesand at least 3 bins were generated. More or less bins could be generatedin response to the indicated values or a total number of bins (e.g.,based on how many boundaries the user wanted to account for). More binscould also be added to account for interactions with a categoricalfactor. For instance in this case a bin is set for the case when just L1is 0 to account for (Max_depth==0 & grow_policy==“depthwise”).Additionally, for validation, a user may want to allow that somecontinuous inputs add extra levels a slight amount above and/or belowthe boundary values. Instead of having the user specify each of theseabove/below/equals individually, the computing system could generatemultiple values or value bins based on just the boundary value.

In this example, the computing system extracted all indicated valuespertaining to the continuous factor from the one or more disallowedcombinations. The computing system determined the multiple value bins bymerging extracted indicated values with possible candidate inputs. Aminimum value for all the value bins corresponds to a minimum value ofthe first set of candidate inputs (e.g., L1=0). A maximum value for allthe value bins corresponding to a maximum value of the first set ofcandidate inputs (e.g., L4 can be 20). A minimum for a respective valuebin is set based on the indicated values (e.g., L3=5). A maximum for arespective value bin is set based on the indicated values (e.g., L3<8).This defines a partitioning scheme for max_depth: [0, 1, 5, 8, 20].

This disallowed combination can be modified to show the new levels(e.g., as shown in graphical user interface 4960 of FIG. 49C).

A value is selected from each of the value bins for generating a testsuite (e.g., a midpoint or an end point in the value bin). Accordingly,a design space for the test suite is constrained by the disallowedcombination or a modification of the disallowed combination (e.g., whena script for the disallowed combination is run by the computing system).

FIGS. 50A-B illustrate example graphical user interfaces for validationspecifications.

In one or more embodiments, the validation specification comprisesmultiple validations or multiple criteria. A computing system can thengenerate output indicating the deviation from the specified result byoutputting the deviation corresponding to one or more criteria. In theexample shown in FIG. 50A, the graphical user interface 5000 shows avalidation specification comprising a first criteria 5010 indicating acrash of the system of operation. The validation specification comprisesa second criteria 5020 indicating a divergence from a specified responsevalue for a response of the system of operation. In this example, thespecified response was a R squared value which is a statistical measurefor a model. If the value is less than 0.5 than approximately half ofobserved outputs can be explained by a model's inputs. A third criteria5030 indicates a divergence from a specified processing metric of thesystem of operation. In this example, the specified processing metric is300 seconds and any time period longer than this would be an unwanteddeviation. An excessively long processing time may be as bad as a crashif the system is hung-up in processing.

The user may be able to specify the response type (e.g., a threshold ormatch target type). For instance, a validation specification maycomprise a request to identify a set of inputs that will exceed athreshold for a response of the system of operation according to a testcase of a test suite. In FIG. 50B, a test suite 5050 shows test casesfor recording responses according to each of the criteria shown ingraphical user interface 5000 (e.g., if the threshold is passed or atarget is reached). The responses may be specified by the user (e.g., torecord pass or fail for displaying to a user). In this example acovering array of strength 2 is selected for a subset of thehyperparameters not involved in advanced options shown in FIG. 42 .

Rankings with multiple validation specifications can be provided. Forinstance, the user may also assign an importance 5040 to each criteriafor weighting test cases. Alternatively, certain criteria may havedefault weightings (e.g., crash may be set to have the highestimportance).

Output indicating the deviation may also comprise a set of testconditions indicated as potential causes for the system of operation toexceed the threshold (e.g., the potential cause ranking shown in FIG.46B). The potential cause ranking or an updated test suite may be basedon the ranking (e.g., importance 5040) within multiple validationspecifications.

One or more embodiments are useful for generating test cases to accountfor blocking factors. A blocking factor may define different testscenarios for testing the system of operation. In the context of testingsoftware, blocks could be different test engineers, operating systems,different computers, or different days of the week for testing. That is,differences in how a software system behaves may be attributable to theunderlying operating systems or underlying hardware differences, orchanges during development to the software system throughout the weekmay exhibit a day-of-the-week effect.

In the case of test engineers, there may be variability in how differenttest engineers approach testing. These differences are often becauseengineers are inclined to notice different issues while testing. Forexample, a particular test engineer may be more likely to notice numericissues, while another may focus more on user interface or graphicalissues. In testing software, different test engineers may choosedifferent data, and this difference would likely further exacerbate thedifferences between test engineer results.

One strategy of assigning test suites among different test engineers isto partition the components of the system into groups, assign each testengineer a group, and have each test engineer test the set of componentsassigned to them, placing emphasis on individual components one at atime. Whereas this strategy may ensure that individual components workas intended, in effect, it treats each test engineer as an independentagent and so fails to take into account efficiencies that could accrueif the entire testing effort was treated as a designed experiment. As aresult, although such a testing strategy may appear reasonable, it isnot an efficient strategy. Another strategy of assigning test suitesamongst different test engineers is to use test engineer strengths orpreferences for assignment which may localize a problem to an engineerand not account for the test engineer's role in a result.

One or more embodiments, ensure that test suites for different testscenarios (e.g., different test engineers) are different. They can bedifferent in such a way that, when aggregated, the overall set of testcases increases coverage by treating test scenarios as a blocking factor(e.g., in test suites generated in response to a validation requestdescribed herein).

FIG. 51 illustrates example test suites 5100. In this example acomputing system (e.g., one or more devices of validation system 4000)generates a representation of multiple covering arrays of strength t.For instance, FIG. 51 shows that each test suite 5100 has an examplecovering array 5102 of strength 2 for factors each taking as input oneof two levels (L1 or L2). In this case, the computing system determinesa covering array of strength t+1. For instance, FIG. 51 shows an exampleof a covering array 5103 of strength 3. As shown multiple coveringarrays comprise different combinations of t+1 factors of the coveringarray of strength t+1. The covering array 5103 is an aggregate of thedata of the multiple covering arrays 5102. In this case there were vlevels of blocking factor related to a test scenario (i.e., v is two fortwo different test engineers), so the t+1 covering array is v times aslarge. The covering array could be a different aggregation. Forinstance, some test rows could have been repeated in each of thecovering arrays 5102. In this case the covering array of strength t+1will be larger than v. The aggregate covering array could also be adifferent strength (e.g., strength t, t+2, or variable strength). Forinstance, a variable strength covering array could be set to be t+m,m>1, by which the software inputs would be specified to have a higherstrength than subsets involving the blocking factors. In the example inFIG. 51 , the covering arrays are orthogonal in that each possiblecombination of t inputs occurs the same amount of times (e.g., onlyonce). Covering arrays described herein can be orthogonal but need notbe.

The computing system can generate test suites 5100 to provide test casesfor testing the system of operation. Each test suite can be assigned toeach test scenario of the multiple different test scenarios (e.g.,assigning each test suite to a different engineer or to be performed ona different operating system or day of the week). The computing systemcan obtain a response 5104 of the system of operation according to eachof the at least two different test suites.

In one or more embodiments, the computing system can generate an outputindicating a deviation from the specified result based on the responseof the system of operation according to each of the different testsuites. For instance, the response could indicate a failure (“1” indifferent test cases). Alternatively, or additionally, the computingsystem could output a most likely potential cause 5106 informed byvariations between responses in the different test cases. In this case,looking only at test case 5100A would suggest an input of L1 for Factor1 leads to failures, but in combination with 5100B the computing systemmake narrow the most likely potential cause 5106 to a combination ofinput of L1 for Factor 1 and input of L2 for Factor 2. The anomaly inthe response 5104 of test case 5100A is likely due to the test scenarioor environment rather than the system itself. This can help focusefforts on further testing or fixing system problems.

In this scenario more than one test condition could be considered anddifferent test cases made for those scenarios (e.g., test engineers andday of the week). Alternatively, or additionally, factors within thetest case could also include blocking factors as described herein (e.g.,assigning different test cases to different test engineers and includingas a factor different test data sets that the engineers will use).

FIG. 52A illustrates example test suite metrics 5200 pertaining to testsuites derived for a system of operation pertaining to an XGBoostsoftware library. Test suites are derived to validate thirty-fourhyperparameters with inputs shown in FIG. 43 .

The continuous factors with bounded ranges shown in FIG. 43A arediscretized into categorical inputs of only two levels as shown in FIG.43E. For categorical factors there is one 6-level factor, one 4-levelfactor, one 3-level factor, and thirty-one 2-level factors. In thisexample, disallowed combinations were not used. However, as describedherein, disallowed combinations (also sometimes called forbidden edgesor disallowed sets of inputs) can be considered in determining a subsetdesign space (e.g., during design construction or in received designinformation) for generating the test suite.

In this example, the blocking factor is test engineers, but otherblocking factors could have been used. There is a pool of eight testengineers available, and it is desirable to weigh how many testengineers to assign to this testing effort while balancing theirworkload. The goal is to provide each test engineer a set of test casesthat is a covering array of strength 2, while the aggregated coveringarray for all test engineers is strength 3. FIG. 52A shows the averagenumber of tests per block for two to eight test engineers (blocks) forthe inputs and factors shown in FIG. 43E. Each of the designs werecreated using the covering array implementation in JMP® Software, with10,000 iterations of a post-optimization technique that tries tominimize the size of the covering array. To achieve a strength 2covering array, the theoretical lower bound on the number of tests is 24(due to one 6-level and one 4-level factor). Similarly, for a strength 3covering array, the lower bound is 72. The JMP® software was able tocreate a design with no blocking factor that achieves the minimum runsize for both strength 2 and 3. Based on these results, only for two andthree blocks would test engineers require additional tests beyond theminimum for a covering array of strength 2. While the lower bound for astrength 4 covering array in this case is 144, the smallest strength 4design had 266 runs, which is much greater than the number of runs shownin the table using an aggregate strength 3 covering array. However, thestrength 3 design still provides good coverage for 4-input combinations.For instance, in the case of 8 test suites, while it does not achieve100 percent 4-coverage (i.e., the percentage of 4-input combinationsthat appear in the test suite), it has 99.77% 4-coverage and 96.86% for5-coverage. Even for 4 blocks, the 4-coverage is 98.24% and 87.77%. As aresult, even if there exists a failure-inducing combination involvingfour inputs, it is most likely covered by the test suite.

FIG. 52B shows an example of editing factors of a test suite. In thisexample, the first 8 factors and inputs shown in FIG. 43E are augmentedby the user in graphical user interface 5230 (e.g., to add additionalinputs known to be more important to the tester or to expand the rangeof tested inputs). Four of these additional factors are at 5 levels andfour at 4 levels. In this setup, there is one 6-level factor, four5-level factors, five 4-level factor, one 3-level factor, andtwenty-three 2-level factors. While the theoretical lower bound for astrength 2 covering array is 30 runs, the smallest unblocked designconstructed by the JMP® software had 37 runs. For strength 3, the lowerbound is 150 runs, while the smallest unblocked design the JMP® softwareconstructed had 244 runs. With the factors and levels in this case, asubstantially larger number of tests are needed as can be seen in testsuite metrics 5260 shown in FIG. 52C.

In the simpler example, in nearly every case, each test suite blockcontained the minimum number of tests for a strength 2 covering array.Increasing the number of blocks in this case increased the size of thetest suite, thereby increasing the 4-coverage. In the more complex case,the increase in the size of the full test suite is smaller relative tothe reduction in the number of tests per block. The ease of adding ablock to a set of inputs allows test engineers to explore differentblock sizes to decide the best allocation of their resources. By usingthe blocking approach to covering arrays described herein, a test suiteassigned to a test engineer can be controlled so that each test engineeris examining all possible two-way combinations of important inputs.

Once a failure is observed according to the test suite, a test engineerwants to isolate the failure-inducing combination. Due to the run sizeeconomy of combinatorial testing, when faced with a failure, a testengineer may have a large list of potential causes (i.e. possiblefailure-inducing combinations) to investigate. As in the hierarchyprinciple for factorial effects, a test engineer wants to focus on thesimplest explanations, the potential causes involving the fewest numberof inputs. As an example, related to the factors in FIG. 43E asaugmented to include more inputs in FIG. 52B, if the combination of treemethod set to auto and feature selector set to thrifty induces afailure, the test engineer has eight potential two-way combinations toinvestigate. A test engineer then must go about determining which ofthose potential causes induces a failure.

In the case where test engineers is the blocking factor, if a failure isinduced in the strength t subset of a particular test engineer, then theengineer knows that all other test engineers have those t-inputcombinations contained within their test suite. If the definition offailure is consistent among test engineers, then all test engineersshould find the failure. For the types of failures that are known to beconsistently checked, this implies that the results from all testengineers can be considered as a whole. Any failure-inducingcombinations involving fewer than t+1 inputs will be easily induced andisolated and those due to at least t+1 inputs can be studied using faultlocalization techniques.

There are failures less obvious to test engineers. In the XGBoostexample, there may be a plot that is incorrect, or a statistic that notevery test engineer has checked. In this situation, the advantage ofblocking is that each test engineer has tested all combinations of tinputs, so failures that rely on a test engineer's individualspecialties will be covered. If a test engineer uncovers a failure thatis not being tested among all test engineers, they can generate a set ofpotential causes based on their test suite. They can then coordinatewith other test engineers to check for the failure condition in one ormore test cases that contain the potential cause or the computing systemcan compare received responses. This can speed up the fault localizationeffort, as if it is due to a t-input combination, one of the other testengineers will induce the same failure. To reduce the burden ofinvestigating all the different potential causes, it is advantageous touse any information about the failures and inputs to start withpotential causes more likely to induce a failure (e.g., most likelypotential cause 5106 of FIG. 51 ). If no other test engineer observesthe same failure, then it can be concluded that the failure is due to acombination involving t+1 or more inputs.

Another example for a block is workday, such as day of the week. Forsoftware development organizations, there may be a number of changesthat are made to the code base daily. A test engineer wants to catch anyfailures introduced by newly submitted code as soon as possible. Withtime and resource constraints, there may be limits to the amount oftests that can be performed on a given day, and a limit to the number oftests to be created. Using the blocking approach described herein with astrength t+1 covering array for the full test suite, if a test engineerobserves a failure on a particular day, they know that for the previousday all t-input combinations were tested. If there were no failures theprevious day (or any failures were deemed fixed), then the observedfailure is most likely either due to a new failure-inducing combinationinvolving less than t+1 factors, or a recently introduced fault thatresulted in a failure that was induced by more than t inputs that hasnot been tested in the previous few days. To determine which situationit is, it is simply a matter of running the failure-inducing test on theprevious day's version of the software. If the test still fails, then itis due to a failure-inducing combination from t+1 or more inputs and canbe tracked back to the day it started to fail. If it does not fail theprevious day, then the failure is due to a new change, and can beinvestigated (e.g., using the hierarchy principle). In this case, a testengineer can combine tests from previous versions that exhibit thefailure with the particular test to aid in fault localization.

One or more embodiments are useful for validating a system in operationand for further analysis if the system in operation fails to meetvalidation specifications. The blocking approach to test suites allowsfor the testing team to achieve the coverage benefits of covering arrayswhile harnessing the testing strengths of individual test engineers. Theblocking approach also aids in fault localization, where test engineersneed to isolate failure-inducing combinations when failures areuncovered during testing.

When validating a system (e.g., a software system, an electrical system,a mechanical system, and a complex system), sometimes there is data orsettings available for testing the system. For instance, a tester of asoftware system may find a dataset in literature or data sets that havecaused problems in the past. However, with this approach the testerwould then need to search for existing data that contain the necessarytesting structures or create their own. These approaches are resourceintensive. Further, if data is taken from public or customer sources, orcreated by a user without a computer-generated approach describedherein, there is no guarantee of sufficient coverage over the entirerange of possible data structures. If there is not sufficient coverage,certain factors or factors of a dataset that cause issues may beoverlooked. This is particularly true where there are a lot of potentialsources of error coming from multiple factors in a very complex system(e.g., FIG. 42 has 34 different user options for hyperparameter tuning).Sufficient and efficient coverage may be particularly difficult when theinvestigator needs to investigate different design tests. In this case,the software may need to process aggregate data structure or data sets.If a complex system is intended to handle a variety of differentsituations or data sets, insufficient coverage of test cases can lead toa system failing to work as intended by a user or investigator when usedoutside of a test case from the investigation or validation.

One or more embodiments provide an effective and efficient way togenerate different data sets that explore a variety of differentsituations for both inputs and outputs. Users can specify differenttypes of input factors, number of runs, ways to generate response(s),etc. This is an improvement over data generation approaches that mayjust generate random data for a very specific set of inputs or generaterandom responses. A computing system can then use the generated datasetsto test the performance of different systems or algorithms to see howthey perform under different varieties of datasets. The computing systemcan be integrated or communicatively coupled with one or more platformsor environments for executing a system. For instance, if the platform isused to execute a software system, one or more options within theplatform can be prespecified and integrated within a test suite thatalso includes data generation. In some embodiments, scripts can begenerated automatically for execution of a system under a data set orsystem options (e.g., for testing a platform according to a designsuite).

FIG. 53 illustrates an example block diagram of a computing device 5302for generating data for a design suite. In one or more embodiments, thecomputing device 5302 is the same, has similar features, or is differentfrom computing devices described herein (e.g., computing device 1302,computing device 3202, and computing device 4002).

For instance, the computing device 5302 can be configured to exchange(e.g., via wired and/or wireless transmission) information related togenerating data for the design suite between devices in a system 5300(e.g., output device 5306 and input device 5304) and/or devices in othersystems described herein (e.g., system 1300, system 3200, and validationsystem 4000). For example, a network (not shown) can connect one or moredevices of system 5300 to one or more other devices of system 1300,system 3200, and/or validation system 4000 (e.g., for receiving andsending messages).

In one or more embodiments, the computing device 5302 receives a request5350 to generate computer-generated data for an experiment. Forinstance, the request 5350 may comprise a request for design data 5352for a design suite (e.g., to explore settings for an electrical,mechanical, or computer system not shown). Additionally, oralternatively, the request 5350 may be a request to validate 4060 asdescribed herein (e.g., whether software will crash under certainconditions or whether the software is providing expected results in atimely manner). The generated data in response to the request cancomprise inputs defining one or more settings for a plurality of factorsfor a design of an experiment. For example, if the experiment is tovalidate that an engine system will not fail, the factors could beenvironmental (e.g., temperatures or humidity and the inputs could bethe degrees and humidity level respectively) or the factors could berelated to the engine itself (e.g., the factor could be fuel and theinputs different types of gasoline that would go into the engine).Additionally, the generated data can comprise generated responsesaccording to the design of the experiment (e.g., how long the engine ispredicted to run under these conditions). For instance, the responsescan be generated based on a model developed for generating a responsefor the generated inputs (e.g., a simulation predicted time) or based ondesired responses from a validator (the car needs to run for at leastthis long before switching from battery operation to gas).

An experimenter may have multiple goals for an experiment. For instance,a validator for an engine system may be interested in ensuring thesystem does not fail and also in considering maximum speeds the enginecan produce. These design goals may have the same or other factors thatinfluence that result (e.g., how fast did a vehicle comprising thesystem go during testing). In this case, there could be multipledifferent design spaces in which the validator would like to ensurecoverage of in designing an experiment (e.g., the candidate inputs inrespective design spaces related to failure and engine performance maydiffer in the number of factors, ranges for those factors, and whetherthe factors are continuous or categorical). In one or more embodiments,the generated inputs and generated responses are generated to berepresentative of a respective design space of multiple different designspaces for the design of the experiment.

In one or more embodiments, the computing device 5302 receives designsuite information 5360 (e.g., for defining different design spaces forthe design suite). For example, the design suite information couldcomprise design information 4050 described with respect to FIG. 40 . Inthe example in FIG. 52 , some design information may be already known orimplicitly known by the computing device 5302 based on the nature of theexperiment. For example, the experiment may test a software platformprovided by the computing device 5302, so the range of inputs to thatsoftware platform may be already known to the computing device 5302operating the software platform.

Some information or requests may be provided by the user or modified bya user (e.g., providing a disallowed combination or specifying one ormore factors using input device 5304). As an example, the computingdevice 5302 may receive first design information 5361 comprising one ormore first characteristics (e.g., a quantity of factors or a quantity ofruns) for specifying generation of computer-generated data associatedwith a first design space of multiple design spaces. The first designspace can define candidate options for generating inputs according to afirst set of factors of factors in an experiment. The computing device5302 may receive second design information 5362 comprising one or moresecond characteristics (e.g., a quantity of factors and the mix ofcategorical and continuous) for specifying generation of thecomputer-generated data associated with a second design space of themultiple different design spaces. The second design space definescandidate options for generating inputs according to a second set offactors of the plurality of factors. The design suite information 5360could comprise information pertaining to more design spaces or only onedesign space. Two design spaces are given here only as an example.

The computing device 5302 could receive information or requests frominput device 5304 or one or more other input devices described herein(e.g., input devices 4004). For instance, a user may use the inputdevices 5304 to enter or select a request or design space information.Alternatively, or additionally, computing device 5302 may generate theinformation or requests (e.g., based on defaults stored incomputer-readable medium 5312 or information known about theexperiment). As an example, JMP® provided by SAS Institute Inc. of Cary,N.C. can generate a list of hyperparameters and possible input values,but also allow users to add additional inputs or factors to define adesign space related to a machine learning algorithm configured by thehyperparameters.

In one or more embodiments, the computing device 5302 receivesinformation or requests via one or more input interface(s) 5308. Theinput interface(s) 5308 could comprise one or more features of an inputinterface described herein or is an input interface described herein(e.g., input interface 1308, input interface 3208, and input interface4008). For instance, the input interface(s) 5308 could receive inputreceived from an external device or internally within computing device5302.

In one or more embodiments, the computer-readable medium 5312 comprisesone or more features of one or more computer-readable mediums describedherein or is one of computer-readable mediums described herein (e.g.,computer-readable medium 4012, computer-readable medium 3212, andcomputer-readable medium 1312). In one or more embodiments, thecomputing device 5302 has a processor 5314. For instance, the processor5314 comprises one or more features of one or more processors describedherein or is one of processors described herein (e.g., processor 1314,processor 3214, and processor 4014).

In one or more embodiments, computer-readable medium 5312 storesinstructions for execution by processor 5314. For example, in one ormore embodiments, the computer-readable medium 5312 comprises a designsuite application 5353 for generating a design suite that providesdesign cases for an experiment. The design suite can comprise thecomputer-generated data that represents settings constrained bydifferent design spaces. As an example, one design suite could comprisea design with different experiment goals (e.g., whether an engine willfail and how fast can an engine drive a car) or the same goal fordifferent sets of design cases but points of analysis (e.g., differentfactors that may contribute to engine failure). The generated designsuite can include computer-generated data that represents, in a firstset of design cases of the design suite, settings constrained by a firstdesign space (e.g., based on first design information 5361), andrepresents, in a second set of design cases of the design suite,settings constrained by a second design space (e.g., based on seconddesign information 5362).

Alternatively, or additionally, the computer-readable medium 5312comprises an experiment application 5344 for conducting an experiment orevaluating an experiment according to the design suite. For instance,the experiment application 5344 can receive one or more resultingresponses corresponding to conducting a first set of design cases and/ora second set of design cases. The experiment application 5344 canreceive the resulting responses by generating (e.g., by testing aplatform on the computing device 5302), input from another system (e.g.,from sensors on an electrical or mechanical system being tested), orinput from a user that conducted the experiments (e.g., environmentalmeasurements). Alternatively, or additionally, the experimentapplication 5344 can evaluate one or more resulting responses accordingto one or more generated responses. For instance, the experimentapplication 5344 can compare a generated response to a responseaccording to the experiment.

In one or more embodiments, the design suite application 5342 orexperiment application 5344 may be performed in response to the request5350. For instance, the design suite output 5370 may comprise a designsuite indication 5372 (e.g., settings, features, or a summary of adesign suite for conducting an experiment). Additionally, oralternatively, the design suite output 5370 may comprise a responseevaluation 5374 comprising one or more evaluations of responsesaccording to conducted experiments for a generated design suite. In oneor more embodiments, the computing device 5302 outputs to an outputdevice 5306 a design suite output 5370.

Output interface 5310 and output device 5306 could be one of or comprisefeatures of output interfaces (e.g., output interface 4010, outputinterface 3210 and output interface 1310) and output devices (e.g.,output device 4006, output device 1306 and output device 3206 describedherein). For example, output device 5306 may comprise a display or aprinter for communicating an evaluation or design suite to a user.Alternatively, or additionally, the output interface 5310 is an internalinterface and feeds information back to the computing device 5302 (e.g.,for conducting an experiment on the computing device 5302).

In one or more embodiments, fewer, different, and/or additionalcomponents than shown can be incorporated into the system 5300 thanshown in FIG. 53 (e.g., components of system 1300, system 3200,validation system 4000 or one or more devices of a particular platformfor which data is generated). For instance, components of input device5304, computing device 5302 and/or output device 5306 can beincorporated into a particular platform device for validatingperformance or functionality of that platform.

In one or more embodiments, the system 5300 implements a method asdescribed herein (e.g., a method 5400 shown in FIG. 54 ).

FIG. 54 illustrates an example flow diagram of a method 5400 forgenerating data for a design suite. An operation 5401 of the method 5400comprises receiving a request to generate computer-generated data for anexperiment. For instance, the request could be one related to aperformance evaluation for a system (e.g., how well a system wouldperform under certain conditions or what inputs may cause a failure).The computer-generated data comprises generated inputs defining one ormore settings for a plurality of factors for a design of the experiment,and generated responses according to the design of the experiment. Inthe example in FIG. 54 , the generated inputs and generated responsesare generated to be representative of a respective design space ofmultiple different design spaces for the design of the experiment. Forinstance, the request could be a user indication to validate, accordingto one or more validation specifications for the multiple design spaces.For example, if a validation specification relates to validating thatinputs would not cause a failure for a system, the generated inputs maybe distributed across candidate inputs of a design space for the system.The generated inputs and generated responses can be used to evaluateresponses conducted according to an experiment performed for the system.For example, if a validation specification relates to determining if asystem performed with a certain expected result or result tolerance, thegenerated responses could indicate an anticipated or allowed deviationbased on testing one or more generated inputs.

An operation 5402 of the method 5400 comprises receiving one or morefirst characteristics for specifying generation of thecomputer-generated data associated with a first design space of themultiple different design spaces. The first design space definescandidate options for generating inputs, of the generated inputs,according to a first set of factors of the plurality of factors. Forexample, a computing system could display a graphical user interfaceprompting a user to specify one or more bounds for generation of thecomputer-generated data associated with a first design space (e.g., aquantity of factors of the first design space, a quantity of runs fordesign cases for the first design space, a disallowed combinationrestricting certain candidate inputs based on assigned inputs for otherfactors of the first design space, and/or bounds on how responses can begenerated). Alternatively, or additionally some design constraints maybe pre-defined, or computer generated (e.g., based on predefinedconstraints of a testing platform).

An operation 5403 of the method 5400 comprises receiving one or moresecond characteristics for specifying generation of thecomputer-generated data associated with a second design space of themultiple different design spaces. The second design space definescandidate options for generating inputs, of the generated inputs,according to a second set of factors of the plurality of factors. Forexample, one or more of the design spaces may differ in a quantity offactors. For instance, the one or more first characteristics maycomprise a first quantity of factors constraining the first designspace, and the one or more second characteristics may comprise a secondquantity of factors constraining the second design space and the secondquantity of factors is different from the first quantity of factors.Additionally, or alternatively, the nature of the factors for one ormore of the different design spaces may be different. For instance, theone or more first characteristics comprise an indication that a firstfactor of the first set of factors comprises a first categorical factorwith a first quantity of categories which defines candidate options inthe first design space, and the one or more second characteristicscomprise an indication that a first factor of the second set of factorscomprises a different factor (e.g., a continuous factor, or a secondcategorical factor with a second quantity of categories different fromthe first quantity of categories and which defines candidate options inthe second design space). A design suite can be generated thataccommodates these different factor sets. For instance, the computingsystem can generate a design suite that comprises a full factorialdesign for factors of the first design space, or a fractional factorialdesign with a subset of candidate inputs from a full factorial designfor the first design space. Additionally, the generated design suite canalso comprise a full factorial design for factors of the second designspace, or a fractional factorial design with a subset of candidateinputs from a full factorial design for the second design space.

An operation 5404 of the method 5400 comprises, responsive to therequest, generating a design suite that comprises the computer-generateddata that represents, in a first set of design cases of the designsuite, settings constrained by the first design space, and in a secondset of design cases of the design suite settings constrained by thesecond design space. For instance, the design suite may comprise a firstset of factors according to a first quantity of factors and a second setof factors according to a second different quantity of factors.Alternatively, or additionally, where a given factor has differentnatures in the different design spaces, the design suite can compriseselected inputs selected from candidate inputs for a first categoricalfactor according to a first design space and selected inputs selectedfrom candidate inputs for a continuous factor or a second categoricalfactor according to a second design space. Accordingly, one or moreembodiments can transform specified characteristics for a respectivedesign space into specific settings in a design suite that a user canuse to conduct an experiment according to the settings. Further, thedesign suite is an improvement over traditional randomcomputer-generated design suites, in that design suites generatedaccording to method 5400 can better account for characteristics or usergoals pertaining to multiple different design spaces.

More or fewer operations could be used to generate a design suiteaccording to the method 5400 shown in FIG. 54 . For instance, operationscould be combined, performed in a different order, or performedsimultaneously. For example, receiving one or more first characteristicsand receiving one or more second characteristics could be receivedsimultaneously after submission of data entered into a graphical userinterface, or submission of a data message or computer instructioncomprising fields pertaining to the characteristics. A computing systemmay receive a request to generate computer-generated data by receivingone or more of these characteristics for different design spaces anddetermining that the characteristics implicitly indicate a request togenerate computer-generated data.

Further, in this example in FIG. 54 , only two sets of characteristicsare provided as illustration, but more characteristics for specifyinggeneration could be provided that are the same or different from theones described herein. Accordingly, the design suite could comprisemultiple different designs. Further, the characteristics could relate todifferent generation features as described in more detail with respectto examples herein.

In some embodiments, a generated design suite can be applied to conductan experiment. For instance, in this example, an optional operation 5405of the method 5400 comprises receiving one or more responsescorresponding to conducting the first set of design cases in theexperiment. For instance, the generated inputs of the design suite maycomprise synthetic inputs created by the computing system for designingthe experiment without real-world or simulated data. Generated responsescan be generated according to the generated inputs. The synthetic inputscan then be used for an experiment to generate real-world or simulatedresponses which can then be evaluated. Additionally, or alternatively,the generated inputs may comprise generated inputs based on or informedby one or more previous experiments of one or more of synthetic inputs,real-world data, and simulated data. For example, the generated designsuite may comprise some pre-existing data and some supplementalsynthetic data. Received responses could be computer-generated accordingto a simulation of a test system or observed responses of a test system.

In some embodiments, an optional operation 5406 of the method 5400comprises evaluating the one or more resulting responses according toone or more generated responses of the computer-generated datacorresponding to the first set of design cases. For instance, acomputing system can compare respective responses of one or more designcases to the generated responses. Additionally, or alternatively,evaluations may be provided or account for one or more resultingresponses according to one or more generated responses of thecomputer-generated data corresponding to the second set of design cases.

In some embodiments, an optional operation 5407 of the method 5400comprises outputting an evaluation of the one or more resultingresponses. For instance, in one or more embodiments a graphical userinterface displays a visual summary of the one or more firstcharacteristics and the one or more second characteristics. The visualsummary may indicate overall or individual performance information fordesign cases associated with the characteristics (e.g., overallperformance for a first set of design cases for the first design spaceand an overall performance for a second set of design cases for thesecond design space). For instance, the set of design cases may be usedto determine whether a system will fail with a variety of inputs and theoverall performance for those design cases may be a verdict indicatingthe system “passed” the tests or may indicate the system ran for overone hour without failure for the set of design cases. Failure could be asystem crash or could be more subtle such as a deviation from aperformance goal or computed metric. Generated data for design suitesmay be particularly useful when a new system or software is introduced.The system can be validated before being used for user data. This isespecially helpful if the user must follow certain guidelines orregulations when conducting its business with the system or software.

FIG. 55 illustrates an example graphical user interface 5500 forcomputer-generated data for an example software platform. In thisexample, a user is given the capability to generate a suite of customdata sets for use in evaluating performance of software features fortesting or validating features in the context of the user's uniqueworkspace.

In this example, the graphical user interface 5500 is for a statisticalplatform. In the case of statistical software, the validation proceduremay rely heavily on the use of datasets to test the analysiscapabilities. These datasets must contain certain structures/featuresthat ensure proper validation. The statistical platform in this exampleis a Fit Y by X platform which requires a data set with an X and a Y asshown in roles control portion 5510. Depending on the selected datatypes for the X and Y (e.g., selected columns of data from selectcolumns control 5520), the output will be different. However, the usermay want to test common functionality inherent to the platform withoutknowing exactly what the input type and output type is. For example, theuser may not have used the select columns control 5520 to specifyparticular roles in roles control portion 5510 of the graphical userinterface (e.g., setting Y, Response 5512 or X, Factor 5514) but maystill want to test functions such as “Local Data Filter”, “Redo”, and“Save Script”, as well as plots that appear once Fit Y by X is used toModel the relationship between two variables. A user may also have otheruser-specified objectives. For example, a user may want to test if thereare issues with using certain features of the statistical platform incombination, such as features related to saving to memory or a diskcertain scripts in combination. As another example, a user may want tomodel how long it takes different operations based on a combination ofparticular data and particular instructions to a platform.

One or more embodiments provided customized data set creation based onuser needs. For instance, a user can select the generate data action5530 to generate a design suite for an experiment based onuser-specified objectives for one or more resulting responses of anexperiment according to the design suite. A computing system can thenevaluate the resulting responses according to the user-specifiedobjectives.

One or more embodiments use a graphical user interface (e.g., graphicaluser interface 5900) to guide a user through differing things that onecan change in a data set creation. Once specified, these can be used toderive inputs to a designed experiment (e.g., a design experimentaccording to a covering array or a Custom Design generated in JMP®provided by SAS Institute Inc. of Cary, N.C.). The designed experimentcan depend on the purpose. For instance, the designed experiment may bedesigned using a covering array to find faults in the minimum runs orthe designed experiment may be a Custom Design approach where a usermight want to model different measures (e.g., performance measures)based on specified criteria. Once the criteria are specified, thecomputing system then creates a data set for each run of the designedexperiment (or provides an option that creates the data set for anygiven row). For example, FIGS. 56A-56B provide an example of data setcreation for this platform shown in FIG. 55 . FIGS. 57A-57B provide anexample of platform or option specific data set creation for the Fit Yby X platform of FIG. 55 .

FIGS. 56A-56B illustrate an example graphical user interfaces for datageneration for inputs and outputs for an example software platform. Inthis example, the tester is testing for software bugs that may generatean error in the software functioning and is using a covering array frompossible X and Y values. In the graphical user interface 5600 the usercan specify different variations for a computing system (e.g., one ormore devices of system 5300) to generate synthetic data. This syntheticdata can be used to give testers peace of mind regarding coverage ofpotential data structures specified by the user because if a coveringarray is used in constructing the design, then it can ensure completecoverage over certain combinations of factors (the strength of thecovering array) with high levels of efficiency.

In the example graphical user interface 5600, the user can specifycriteria related to generating data for inputs, outputs, and the numberof runs in the design suite for different design spaces for the Fit Y byX platform. For instance, the user can specify that the input variable Xcan be one of continuous, categorical, or discrete numeric. Discretenumeric factors can sometimes be referred to as ordinal factors. Theuser is also able to specify the number of levels for categorical ordiscretized continuous inputs (e.g., 2, 3 or 10 levels). The user canspecify the balance of inputs for sets of design cases in the designsuite (e.g., balanced, unbalanced and random inputs for the factors).For instance, if a factor had two levels that could take one of L1 orL2, a balanced set of design cases would have the same or nearly thesame number of L1 or L2's across all design cases of the design, whereasan unbalanced design may have more D's or more L2s, and a random set ofdesign cases could be generated without regard to the balance. Thesesame kinds of criteria can be specified for the output (Y) to ensurebalance in output. This can be useful for when the user is checking aparticular result from an experiment like certain performance criteriaand wants to ensure testing of certain inputs that are likely to producethose desired criteria. In this example, the user was able to specifydifferent numbers of runs (10, 50 and 1000) for different designsaccording to criteria for the input (X) and output (Y).

These values for the different data generation criteria could bespecified as defaults that the user can augment (e.g., adding more orfewer values, or removing a factor in data generation using removecontrol 5602). Alternatively, or additionally, the user can add factorsof pre-defined type using the add factor control 5606 or add a factordefined by the user (e.g., using textbox 5604). For instance, if theexperiment is going to be conducted by another system, a user may wantto specify blocking factors such as another operator or environmentalfactor for conducting the experiment.

In graphical user interface 5600 the user confirms or configures thetest structure (e.g., setting x and y types, levels and balances, andthe number of runs for the design test). Once the tester has determinedthe data structure factors and desired level of coverage, a design suiteis generated.

The computing system can display the generated design suite to the user.Alternatively, or additionally, the computing system can display avisual summary of characteristics for specifying generation of data.FIG. 56B shows an example graphical user interface 5630 displaying anexample visual summary of different characteristics for generating thedesign. Each of the rows of this visual summary is associated with a setof design cases, where each set of design cases has 10, 50 or 1000design cases or runs in it. For example, row 5632 summarizes a first setof design cases that correspond to characteristics for data generationcorresponding to a first quantity of design runs (i.e., 10 runs), androw 5634 summarizes a first set of design cases that correspond tocharacteristics for data generation corresponding to a second quantityof design runs (i.e., 50 runs). The design suite corresponding to thisvisual summary in graphical user interface 5630 can have 5,300 designcases with a first set of design cases corresponding to the 10 runs anda second set of design cases corresponding to the 50 runs. The visualsummary advantageously presents a consolidated view of the importantfeatures of a much larger design suite. The user can also get a holisticresponse for these different sets of design cases in a response column5636 (e.g., an outcome of an experiment for that set of design cases orwhether all the design cases in the set met or exceeded a predicted orexpected generated response).

In this example, the user saves a great deal of time by using thesynthetic data, because the user did not need to spend extensive timetracking down or creating appropriate datasets, this one having 5,300design cases as part of it. There may also be factors needed for datageneration or one or more user objectives that the user has notspecified. For example, FIG. 56C shows a graphical user interface 5660of an expanded visual summary containing the information in FIG. 56Bwith additional information 5662 generated by the computing system inthe absence of user supplied information. For instance, the computingsystem has assigned different options related to getting a response suchas whether to save a script to data table, journal, window, log, reportand/or a clipboard, and whether to redo analysis or copy a graph.

Embodiments are useful where a user has first specified a system (e.g.,when embodiments are integrated with a software platform, or can receivean indication of a machine learning algorithm). In this case,information may be known to the computing system regarding the types ofinputs, responses, and options that can be changed for that platform.Instead of being in a design space with infinite options for factors,design space options can be adjusted or narrowed for that platform, andsome options for the platform can be pre-defined or pre-loaded (e.g.,receiving characteristics based on receiving an indication of a softwareplatform or machine learning algorithm associated with factors in thedesign suite). For instance, the user can specify a platform (like theFit Y by X platform), and the computing system can provide a list ofdata set factors and platform factors for that platform or algorithm.The computing system can receive one or more resulting responsesaccording to an experiment based on a generated design suite responsiveto submitting the settings constrained by a design space of the designsuite into the software platform or algorithm.

FIGS. 57A-57B illustrate example graphical user interfaces forplatform-specific data generation for an example software platform forthe Fit Y by X platform. In graphical user interface 5700, the list ofdata set factors and platform factors can be predefined. The user couldaugment, add, or remove factors as in FIG. 56A. In graphical userinterface 5750, the computing system has created a design that takes thefull set of data set and platform factors. Each row of this designprovides a different data set and inputs to the platform. For each row,the computing system can generate a script that takes the data setdefined by that row and runs it in the platform with the options givenin that row.

In this example, platform-options are generated, and synthetic datagenerated for the platform. In this case the design suite summarized ingraphical user interface 5750 was generated by Custom Design in JMP® fora continuous response variable. Other tools for design suite generationcould have been used.

Some platforms have more complicated options than the example shown withrespect to the Fit Y by X platform. For instance, JMP 16 provided by SASInstitute Inc. of Cary, N.C. has a Prediction Profiler that allows auser to interact with a statistical model. For example, a user cangenerate predictions from a model responsive to changing settings forinputs to the models in an interactive graphical user interface for thePrediction Profiler. For instance, the interactive graphical userinterface can comprise an interactive matrix of plots. An interactiveplot in the matrix could show an independent variable on the x axis anda dependent variable on the y axis. The interactivity of the plot couldsupport a user's ability to drag a vertical line in the plot to change acurrent x-value. This action could force all the plots in the matrix toredraw (e.g., continuously) with the dragged line. For example, if thereis a two-factor interaction involving the factor whose value ischanging, then the slope of the function for the other factor involvedin the interaction can change simultaneously.

The user can use the Prediction Profiler to find particular settingsbased on certain goals (such as maximizing an optimality criterion). Forexample, if a user was looking at a criterion such as predictinghardness of a substance, the inputs in an interactive plot of thePrediction Profiler may pertain to certain components of that substancesuch as silica, silane, and sulfur. Using the interactive plots, theuser could increase or decrease silica and see how that would affecthardness and find settings for all three components that would maximizehardness. Prediction Profiler also allows users to set disallowedcombinations for a model as described herein. Checking functionality ofthis platform in a meaningful way can be difficult as the profiler needsto display correctly under different settings. Further, the profilerallows users to select different goal options such as maximizedesirability, and the profiler will need to present a point that iseither optimal or close to optimal which may not be known.

FIGS. 58A-58B illustrate an example of a graphical user interface for aPrediction Profiler platform that sets disallowed combinations for adesign space of a design suite. FIGS. 58A-58B show a row of plotsshowing the conditional relationships of a dependent variable (Y)against all the independent variables (X1, X2, X3). That is, thefunction shown in each plot is the graph of the conditional relationshipof the plotted x-variable at fixed values of all the other x variables.Vertical lines (e.g., vertical line 5802) in each plot fall onto thex-axis at the current fixed x-value. All the current x values aredisplayed below the relevant x-axis. Horizontal lines (e.g., horizontalline 5804) across each row of the matrix fall on the y-axis at thepredicted y-value which is displayed to the left of each y-axis. In thisexample, there are independent variables including a 2-level categoricalfactor (X1) with levels L1 and L2, and for both X2 and X3 there arecontinuous inputs possible between −1 to +1. In this example disallowedcombination, there is a restriction that when X1=“L1”, X2 cannot bebetween −0.5 to 0.5. The disallowed combination for a data generation isset and visualized with the Prediction Profiler. FIG. 58A shows agraphical user interface 5800 showing the possible values for X2 and X3when X1=L2. FIG. 58B shows a graphical user interface 5850 showing thepossible values for X2 and X3 when X1=L1. As shown in FIG. 58B, there isa gap 5852 in the possible values for X2 under the conditions shown ingraphical user interface 5850.

FIG. 59 illustrates an example of a graphical user interface 5900 forspecifying characteristics for design spaces of a design suite in themore complex example. In this example, the design suite is used to setup data set creation and test options for the Prediction Profiler.Considerations are given to a variety of areas for the PredictionProfiler such as the number of factors, types of factors, number oflevels, distribution of factors (i.e., draws from a distribution vs. adesigned experiment), number of runs, missing values, number ofresponses, generation of responses, multiple disallowed combinationconstraints, etc. In this example, where there is a variety of types ofdata, it quickly becomes problematic to ensure that a design suite hasrepresentative data sets that cover the range of data that can beexpected in the field. It is useful that these problems are found beforethe system is used for real-life data.

FIG. 60 illustrates an example graphical user interface 6002 on acomputing device 6000 for specifying execution of the design suite. Inthis example, a tester can generate a super-table that guarantees adesired level of coverage through a suite of computer instructions forcreating individual datasets. This enables a user to create suites ofdatasets (some potentially very large) using a graphical user interfaceand select an order of execution of design cases (e.g., by clicking orselecting options or buttons of a graphical user interface). Forexample, a computing system can generate a design suite by designing acovering array for different sets of factors pertaining to differentdesign spaces and receive one or more resulting responses according to aset of inputs of the covering array. An evaluation of the one or moreresponses can be displayed in a visual summary (e.g., indicating afailure or passing of design cases).

In the example in FIG. 60 , the computing device 6000 displays in avisual summary 6020 evaluations performed for a design suite based oncharacteristics for specifying generation of computer-generated dataassociated with different design spaces (e.g., according tocharacteristics specified in FIG. 59 ). Rather than show the specificspecified conditions for generating a set of design cases, thesecharacteristics are shown implicitly by design space information indesign space column 6070. Evaluations for each of the set of designcases corresponding to a design space are recorded in response column6060 as the execution of a set of design cases are completed. Theevaluations pertain to performance of the Prediction Profiler as anexample, and responses are indicated indicating different responsespertaining to performance such as (“pass”, “fail”, margin of deviationof “>5%”). The user has specified an order of completion of a subset ofdesign cases in the design suite by ranking the sets in an order column6080. For instance, the user can use the track pad 6006 and keyboard6004 to rank an order of certain ones of the design case sets of adesign suite by selecting a particular visual representation ofdifferent characteristic associated with different design spaces. Theuser has received three evaluations according to user selection in thevisual summary pertaining to characteristics sets defining differentdesign cases.

The user selection of different orders of experimentation for the setsof design cases enables assignment by the computing system of differentcomputing nodes for execution of the experiment. Subsequently, ordereddesign cases may be capable of executing multiple cases in the same ordifferent sets in parallel.

In one or more embodiments, a user can select or mark other features ofthe visual summary 6020. For example, the computing system responsive toa user selection of a particular row has associated an indicator 6018visually indicating a feature or quality of a dataset. For instance, adataset may have been investigated, or used before, and the user maywant to mark them as being “verified” as to their usefulness. As anexample, a verified data set can be used to indicate data sets that havebeen useful in the past (e.g., data sets that were able to produce afailure in a system). They can also be used to indicate or to savecharacteristics for data sets that may be useful in the future. Forinstance, characteristics pertaining to generation of data may be savedto a construction library so that a computing system can generate dataor a design suite from instructions for generating the verified data setfor a subsequent design suite, without having to save the actual designcases.

Once a table is built as shown in FIG. 60 . A user can select to savethe table (e.g., by selecting a save table option 6030 to save a tableto a disk or memory). The computing system can then save instructions tocreate the data tables without needing to store the actual generatedinputs. This can save storage resources. Information or instructions forgenerating a dataset can take up less memory or disk storage thangenerated inputs according to the information or instructions. This toolcan then be used not just by testers but also by other groups such asresearchers developing new techniques and algorithms. The researcherscan share simulated data tables as supplementary material to theresearch to encourage replication and further research. Alternatively,or additionally, different data sets could be stored in a project thatkeeps data available and allows a user to select through the differentcases (data sets) that have been created. In this example, an indicator6018 is used to mark a set of design cases that have been “verified” insome way (e.g., options have been evaluated and shown to result in afailure).

FIG. 60 presents a visual summary with simply a reference to 12different design spaces. The visual summary could have been presenteddifferently such as with explicit references to the characteristics thatgo into data generation associated with those design spaces. Forexample, FIGS. 61A-61C illustrate an example graphical user interface ofa visual summary of a design based on a strength 2 covering array forspecified factors of design spaces in the visual summary 6050 of FIG. 60. As in FIG. 60 a computing system can be used to generate a data setfor each specified row (e.g., Custom Design provided by SAS InstituteInc. or a random distribution tool). In this display format, the usercan easily see that each of the rows of the graphical user interfacedescribes the factor type for each of different factors, specifiesdisallowed combinations, specifies options for generating the responses,and specifies options for testing the prediction profiler. For instance,in FIG. 61A, portion 6100 of the graphical user interface showssummarized computer generated criteria for inputs for factors (e.g.,X1-X10) (e.g., according to user criteria in FIG. 59 specified for X1-X4and remaining factors). In FIG. 61B, portion 6130 of the graphical userinterface shows different computer-generated response criteria andconstraints. In FIG. 61C, portion 6160 of the graphical user interfaceshows additional computer-generated constraints.

For instance, the user can specify one or more characteristics thatcomprise one or more constraints or distribution requirements forgenerating inputs for a first set of design cases in the design suite.As an example, FIG. 61B shows disallowed combinations in column 6133 fora respective design space. The computing system can refrain, accordingto the constraint from selecting inputs for a respective design case ina constrained region of candidate inputs. For example, for the firstdesign space, the factor X1 cannot be assigned an input of 1 in a designcase corresponding to design space 1 when X2 is equal to 1. Some of thedesign constraints are expressed in a continuous form. For example, inFIG. 61C, column 6161 has continuous constraints for respective designspaces. In this case, the computing system may modify a receiveddisallowed combination for processing of assigning inputs (e.g.,discretize candidate values for X1 and X2 to determine bounds forassigning inputs in the design suite).

As another example, FIG. 61C shows distributions requirements for designcreation of inputs regarding considering interactions and main effectsof factors in column 6162. This can control how inputs are assigned ordistributed to isolate certain factors or certain combinations offactors (e.g., by selecting or refraining from selecting certaininputs).

Alternatively, or additionally, characteristics for specifyinggeneration of computer-generated data comprise requirements forgenerating responses (e.g., different response types). For example, ifthe user response goal is related to maximizing an output, FIG. 61C,specifies options for responses of the Prediction Profiler such aswhether to maximize and remember in column 6163 and the extent tomaximize desirability in column 6164. As another example, in FIG. 61Bthe options include number of responses in column 6131 (e.g., 1, 3 or5). Further, the different sets of design cases can have different usergoals in the Prediction Profiler. For instance, response goal column6132 provides different options for different response types in thePrediction Profiler such as Maximize, Random, Match Target, andMinimize. Accordingly, the visual summary can receive different types ofresponses based on specifications and this can be independentlyevaluated by the computing system. Distinct evaluations can be displayedin the visual summary 6020 in FIG. 60 .

Each of these sets of design cases can have different numbers of designcases as specified. For instance, FIG. 61C shows a run size column 6165with run sizes selected of either 32 or 64.

The user can have the flexibility to specify other factors than what isshown here. For example, the user could add a factor that determines theseed used to generate random output or even a factor that determines howthe data table is generated (i.e., full factorial vs. fractionalfactorial designs). Accordingly, embodiments herein provide flexibleinteractive graphical user interfaces for a user to specify criteria forgenerating data even with complex platforms and to view evaluationsperformed according to that generated data. Should an evaluationindicate a problem with one or more design case in a design set,embodiments enable graphical user interfaces for detecting problematicdesign cases.

FIGS. 62A-E illustrate example graphical user interfaces for designcases corresponding to a single design space of multiple differentdesign spaces of a design suite corresponding to the specifications inFIGS. 61A-61C. In FIG. 62A, graphical user interface 6200 showsinformation pertaining to the full design suite as defined by the user(e.g., with 12 rows corresponding to each of the design spaces andinformation on 10 specified factors). The user can select one or more ofthese set of design cases that make up a design suite for furtherexploration (e.g., for localizing the source of a problematicevaluation). In FIG. 62A, the user has selected one row of the visualsummary which corresponds to a set of 64 design cases. Graphical userinterface 6220 displays information pertaining to that set of selecteddesign cases. The design cases in this set can be individually augmentedby the user in the graphical user interface 6220. For example, the usercan exclude design cases or add in additional disallowed combinations.It is noted that in FIG. 59 the user specified particular value optionsfor factors X1-X4 (i.e., 3-level discrete numeric and 3-levelcategorical) and particular value options for remaining factors (i.e.,continuous, categorical, and mix). In FIGS. 62B and 62C, the computingsystem has synthesized inputs for 12 factors corresponding to designspace “5” in design space column 6190 in FIGS. 61A-C. For instance,there is input data generated for 64 design cases and 3 response valuessimulated. FIGS. 62B-62C show portions of a graphical user interface6240 containing the specified input values for the 12 factors and the 64runs corresponding to this one set of design cases of the design suite.FIGS. 62D and 62E show a graphical user interface 6260 which displaysexample generated response values in columns 6262 for 3 responses foreach of the 64 runs according to the design. The generated responsevalues can be used for evaluating responses according to the generatedinputs in FIGS. 62B and 62C. Rather than the user having to review the576 design cases in a design suite, the user can view the visual summaryof the design suite with a holistic evaluation, and hone on a particularsubset of design cases for further analysis (e.g., design casesassociated with a failure indication). Accordingly, one or moreembodiments provide an improved technique for generating data accordingto different design spaces and improve user interaction with designsuites comprising the generated data and experiments conducted accordingto the design suites.

One or more embodiments are useful for generating a design for a testsuite (e.g., a test suite for validating a system in operation or fortesting as part of an experiment described herein).

FIG. 63 illustrates an example design system 6300 for outputtingsettings for a system under test. A “system under test” is a term of artfor a system that is, for example, being tested, is operable fortesting, or is intended to be tested (e.g., according to a design). A“test system” as used herein can include a “system under test”. In thisexample, the computing device 4002 (described in more detail withrespect to FIG. 40 ) can receive a design request 6360 (e.g., via one ormore input interface(s) 4008). The design request 6360 can be a requestfor a combinatorial test for a system under test. The design can includedifferent test cases for testing the system under test. Each test casecan include multiple test conditions for testing one of factors in thesystem under test. A test condition of the multiple test conditions caninclude one or more options for a given factor in the system under test.

A combinatorial test is a testing technique in which multiplecombinations of input parameters are used to perform testing. Forinstance, if the design is for validating a system, the design request6360 may be, or include, a request 4060 to validate the response of asystem under test. A combinatorial test could be useful for testingcombinations of factors to see if this would induce a failure in thesystem under test.

The one or more input devices 4004 described herein could includedevices for receiving user input regarding the design request 6360. Forexample, the input devices could include a graphical user interface 6320for displaying and receiving the design request 6360.

In one or more embodiments, the computing device 4002 receives designinformation 4050. In this example, the device information includesfactor information 4052. Factor information 4052 could be a factorindication of a total quantity of the factors for the design. Forinstance, the factor information 2052 could be specific factors thatwould be a part of the requested design or a total quantity.

Additionally, or alternatively, the design information 4050 includes astrength indication 6352 for a covering array. The covering arraycomprises all combinations for any subset of t factors where t is anumerical value. A strength t covering array guarantees that allcombinations of t or fewer inputs occurs in the test suite. Mostresearch for covering arrays involves finding a covering array ofstrength t, where the covering array includes the smallest number ofruns (also referred to as test cases or tests) as possible to guaranteethat all combinations of t or fewer inputs occurs in the covering array.This is ideal when performing or generating a test case is veryexpensive (e.g., when testing involves flying an airplane to conducteach test case). However, using the minimum number of runs makes itdifficult to find the exact failure-inducing combination when failuresdo occur in a test suite. Having additional test cases above the minimummay be useful (e.g., to increase the t+1 coverage of the test suite andimprove fault localization). Embodiments herein allow a user to specifythe number of runs in the test suite or have the smallest possibledesign to satisfy one or more design requirements (e.g., a particularstrength for the design).

For instance, additionally, or alternatively, the design information4050 includes run information 6350. For instance, the run information6350 can be a run indication of a total quantity of test cases for thedesign (e.g., a minimum or user-defined quantity). The run information6350 could include restrictions on the test cases (e.g., disallowedcombinations or specific test cases to include). For example, if thedesign is for testing a manufacturing process, there may be somecombinations of factors that would not be possible, or may be needed, inthe real-world experiment conducted from the design.

In one or more embodiments, the test suite application 4040 uses thedesign information 4050 to generate a design. The design can be apredefined design, a generated design, or can be an updated design froma generated, predefined, or updated design. For example, the test suiteapplication 4040 can be used to modify one or more test cases from afirst design, select one or more test cases to remove from the firstdesign, and/or add one or more computer-generated test cases to thefirst design. For instance, if a first design includes a set of testcases that represents a covering array according to the strengthindication 6352 but comprises more or fewer test cases than is neededaccording to the factor information 4052, the first design can beupdated to have an updated design constrained to the design information4050 (e.g., having a total quantity of test cases as indicated by therun indication). A first design used to generate an updated design coulditself be an updated design. It is referred to as first examples hereinbecause the design is an initial or previous design used to generate anupdated design. It need not be the first in-time or first in a series ofgenerated designs.

In one or more embodiments, the computing device 4002 can output a testsuite 6370 according to a design generated by the test suite application4040. For example, responsive to receiving the request 6360 for thedesign of the combinatorial test for the system under test, thecomputing device 4002 can output a respective setting for each testcondition for at least one test case of the updated design for testingthe system under test (e.g., via the output interface 4010 to an outputdevice 4006). Additionally, or alternatively, the computing device 4002can output the test suite 6370 to other applications of the computingdevice 4002 or output device 4006 (e.g., if the design system 6300 ispart of a validation system 4000 of FIG. 40 ). The computing device cangenerate responses 4044 of a tested system or find deviations from atest specification using the deviation application 4042 described inmore detail herein. Embodiments herein allow a user to have greatercontrol over a computer-generated design and subsequent testing fromthat design (e.g., by specifying an amount of test cases greater thanthe minimum).

In one or more embodiments, a computing system (e.g., the design system6300 or computing device 4002) implements a method as described herein(e.g., method shown in FIGS. 41A-B and FIG. 64 ). FIG. 64 illustrates anexample method 6400 for outputting settings for a test system. Method6400 includes an operation 6401 of receiving using a graphical userinterface a request for a design of a combinatorial test for a testsystem. The design comprises a plurality of test cases for testing thetest system. Each test case of the plurality of test cases comprisesmultiple test conditions for testing one of factors in the test system.A test condition of the multiple test conditions comprises one ofoptions for a given factor in the test system. The method 6400 caninclude operations for receiving additional information relevant to therequested design. For instance, method 6400 includes an operation 6402of receiving using a graphical user interface a run indication of atotal quantity of the plurality of test cases for the design. Method6400 further includes an operation 6403 of receiving using a graphicaluser interface a factor indication of a total quantity of the factors.Method 6400 includes an operation 6404 of receiving using a graphicaluser interface a strength t for a covering array. The covering arraycomprises all combinations of t factors. These operations are by way ofexample only. One of ordinary skill in the art will understand thatthese operations could be one operation (e.g., received throughsubmission of information in a graphical user interface), or could occurin different orders (e.g., a sequence of windows for receivinginformation). Additionally, or alternatively, more or fewer operationscould occur. For example, the method 6400 could involve receiving moreor fewer constraints on generating the deciding.

Method 6400 includes an operation 6405 of generating an updated designby selecting, by the computing system, of one or more test cases toremove from a first design; or adding, by the computing system, one ormore computer-generated test cases to the first design. In this example,the first design comprises a set of test cases that represent thecovering array according to the strength indication. The set of testcases comprise more or fewer test cases than the total quantity of theplurality of test cases. The updated design is constrained to the totalquantity of the plurality of test cases as indicated by the runindication. In other examples, the first design could have the requestednumber of test cases, and test cases could be modified.

Method 6400 includes an operation 6406 of responsive to receiving therequest for the design of the combinatorial test for the test system,outputting a respective setting for each test condition for at least onetest case of the updated design for testing the test system. One or moreembodiments allow for repeating operations in method 6400. For instance,after a generated design under operation 6405, or output settings underoperation 6406, a user could update the design information to receiveanother updated design. Accordingly, a first design could itself be anupdated design and the updated design a further updated design.

Updating a design can be useful for a user to explore benefits ofdifferent designs. For instance, the user can observe if increasing anamount of test cases would improve an output design (e.g., prior toconducting the design). As an example, in a design for testing waterquality in a region by a monitoring station, a user could see whethertaking more samples of the water quality would improve testing diversityor covering array coverage and by how much. Additionally, oralternatively, a user could evaluate whether more factors (e.g., moretested pollutants) could be conducted without sacrificing too much onperformance metrics (such as testing diversity or covering arraycoverage). Settings for a system under test may be output (e.g., to agraphical user interface) with metrics or statistics to aid a user inmaking that evaluation. Additionally, or alternatively, the user couldset thresholds or tolerances (or use predefined defaults) forperformance metrics for a computing system to request updated designinformation (e.g., if the design requirements cannot be satisfied).

FIGS. 65A-65B illustrate example graphical user interfaces for usercontrol of design information (e.g., design information 4050). In FIG.65A, graphical user interface 6500 shows example default values that canbe specified by a computing system (e.g., a default value for thestrength indication 6502, or a default number of factors and levels forthe factor indication 6504). Alternatively, or additionally, thegraphical user interface 6500 allows for user input specifying in thegraphical user interface 6500 one or more custom values for the designinformation. For example, the user can add factors (e.g., increasing thenumber of factors to more than ten factors) using the add factorsoption. As another example, the user can edit the strength indication6502 (e.g., using editable text boxes to increase the t numerical valuecurrently equal to 3 to be greater than 3 or less than 3).

Graphical user interface 6500 has a continue option 6506 for the user tocontinue to add or select additional design information. For instance,FIG. 65B shows a graphical user interface 6550 with updated user-definedinformation based on the changes made to default values in graphicaluser interface 6500. For example, the roles of the factors have beenchanged from categorical to continuous. The category levels have beenreplaced with corresponding ranges of possible values for the nowcontinuous factors. The graphical user interface 6550 also has placesfor specifying an amount of test cases (i.e., runs) for a design. Inthis example, the computing system generated and displayed a minimum6556 quantity of test cases or runs for the computing system to providethe covering array according to the strength indication 6502 in FIG.65A. In this example, the user has selected a default 6554 thatindicates a total quantity of test cases that is more than the minimumquantity of test cases. The user could have also supplied auser-specified 6552 number of runs.

In the example of FIG. 65B, the user can select the make design option6558 to request a design. The computing system can receive, using thegraphical user interface 6550, design information comprising the runindication, the factor indication, and the strength indication formaking the design. For instance, the computing system can retrieve oneor more preconfigured default values for the design information. Forexample, the computing system can retrieve the default number of rows.Alternatively, or additionally, the computing system can receive userinput explicitly specifying in the graphical user interface one or morecustom values for the design information or restrictions on the design.For example, the user has customized the factor options in in factorregion 6560. Additionally, factor constraints region 6562 allows a userto specify controls for disallowed combinations. The user can alsoselect coverage criteria 6632 for controlling the coverage of thedesign. FIG. 66B describes in more detail examples of coverage criteria6632 options.

FIG. 65B shows a static default value and a dynamically updated defaultfor user consideration for a run indication. For instance, FIG. 65Bshows a suggested amount of test cases (e.g., a minimum 6556 of runs).The minimum 6556 shown can dynamically update (e.g., based on a quantityof factors or a strength indication). For example, if the user adjuststhe number of specified factors in factor region 6560, the graphicaluser interface 6550 can dynamically change the suggested amount of testcases (e.g., a minimum 6556 of runs in response to receiving the factorindication. Other factors not shown can also be used to dynamicallychange the suggested amount of test cases (e.g., changing the strengthindication).

FIG. 66A shows a portion 6600 of graphical user interface 6550 showing adynamically updated quantity of runs based on one or more differentrequirements. In this case, the minimum 6505 has been increased to 64from 11. For instance, the minimum 6556 could have changed due to anincrease in a quantity of factors or a strength, which would requiremore runs to find a required covering array.

In one or more embodiments, a user can optimize a generated designaccording to different criteria. FIG. 66B shows a drop-down options list6630 allowing a user to specify coverage criteria 6632 (or optimalitycriteria). The optimality coverage criteria 6632 in FIG. 66B include anoption to maximize the t+1-coverage. If the user sets a strengthindication to t than the minimum number of test cases would be thenumber of test cases to have combinations of t factors satisfy thatstrength indication. Maximizing the t+1 coverage would allow the overalldesign to achieve as best as possible coverage of t+1 combinations(e.g., by adding additional test cases). The optimality coveragecriteria 6632 include maximize the weighted (t+1)-coverage. This wouldallow the user to favor certain combinations (e.g., combinations offactors) as explained in more detail with respect to FIGS. 67A-67B. Theoptimality coverage criteria 6632 includes minimize the maximumconfounding number. For a t-input combination, the confounding numbercan be defined as the number of other t-input combinations that occur inidentical rows. By improving confounding numbers, this would allow adesign that better explores the interaction between factors as explainedin more detail with respect to FIGS. 68A-68D. The optimality coveragecriteria 6632 include maximize the t-diversity. The diversity is a ratioof the number of combinations of the factors covered to a total numberof the factors. The coverage criteria 6632 can be used to optimize, byfor example, identifying and modifying don't care cells. Don't carecells include cells of a test suite where the test condition of thatcell can be set to any value and not change the strength of the coveringarray for the test suite.

In one or more embodiments, a computing system can consider more thanone coverage criteria 6362. For example, options column 6634 allows auser to select and/or provide a prioritization of considered criteria.FIG. 66C shows an example in which two criteria are considered.

FIG. 66C shows an example in which a computing system displays, in thegraphical user interface 6660, two design windows 6670 for usercomparison of the designs. In this example, the strength indication andnumber of specified factors is the same for both designs. However, asshown in the metric regions 6672A and 6672B the number of test cases (orruns) specified is different. For instance, first design window 6670Aspecifies a number of runs of 64 and second design window 6670B, for anupdated design, specifies a number of runs of 74.

The computing system can specify for a design one or more metricsindicating a performance of a design. In this example metric region6672A shows diversity metrics and coverage metrics regarding thecoverage in the design of combinations of the factors. The diversity isa ratio of the number of combinations of the factors covered to a totalnumber of the factors.

In one or more embodiments, the computing system can receive, using agraphical user interface, a user indication to change the total quantityof multiple test cases for the requested design (e.g., to generate thesecond design window 6670B). Responsive to the user indication, thecomputing system can output updated settings for testing the systemunder test, accounting for the change to the total quantity of themultiple test cases (e.g., in increased number of runs). The metrics canalso be updated in response to the different test cases. For example,the metric region 6672B shows coverage in the updated design ofcombinations of the factors and diversity in the updated design. Byproviding this comparison, the user can see that the increase in numberof runs slightly increased the coverage but worsened the diversity. Fromthis information, the user can make decisions on whether to have theincreased number of runs in testing. Other metrics could be used (e.g.,confounding of factors in the updated design described in more detailwith respect to other examples).

Additionally, or alternatively, the measures of design quality (e.g.,the coverage and diversity metrics) can be used for determining aparticular design. For example, a computing system can compute aproportion of coverage in the initial design for combinations of sets ofthe factors. The sets each include more factors than required by thestrength indication of the design. For instance, if the strengthindication indicates coverage of a combinations of t factors, thecomputing system can compute a portion of coverage of combinations offactors equal to or greater than t+1. In this example, the metric region6672B shows a t+1 coverage (t=3 in this case) of 90.67 indicating that90.67 percent of combinations comprising 3 factors would be in thedesign. As another example, the metric region 6672B shows a t+2 coverageof 68.84 indicating that 68.84 percent of combinations comprising 4factors would be in the design. The computing system can generate theupdated design by one or more of removing, modifying, or adding a testcase to increase the proportion of the coverage of the initial designand have 74 runs. The coverage values shown in the metric region 6672Bcan represent an optimal coverage that the computing system found ingenerating an updated design. An optimal value (e.g., an optimalcoverage) may not be the best possible for a particular type of runs ifthe computing system is considering other criteria.

For example, additionally, or alternatively, the computing system cancompute a diversity metric for the initial design and generate theupdated design by removing or adding test case to increase diversity ofthe updated design compared to the diversity metric for the initialdesign. The diversity values shown in the metric region 6672B canrepresent an optimal diversity that the computing system found ingenerating an updated design.

In one or more embodiments, the computing system sets a processingconstraint for generating the design. For instance, in FIG. 66C, thecomputing device is limited by the processing constraint 6676 toproviding a maximum of 250 iterations to find the best design accordingto the optimality criteria. Other processing constraints could be set,such as a maximum processing time, or no processing constraints provided(e.g., for designs with a small number of factors). Having processingconstraints can be useful in situations such as where there are morethan ten factors and the complexity of any design increases to the pointthat, even for a computer, it is too difficult to timely and accuratelyfind a design under the constraints.

The test cases in FIG. 66C can be used for automated testing. Forinstance, the computing system can submit settings for one or more testcases (e.g., according to an updated design described herein) into thesystem under test and receive, using the graphical user interface, oneor more resulting responses corresponding to conducting, with the systemunder test, a design. For example, a JMP Scripting Language (JSL)scripts can be written for unit testing in JSL applications. FIG. 44 ,described in more detail herein, shows an example graphical userinterface 4400 showing example responses from conducting an experimentaccording to a design.

In one or more embodiments, a computing system can receive auser-defined specification for one or more resulting responsescorresponding to conducting, with the system under test, a design. Thecomputing system can receive an evaluation of the one or more resultingresponses according to the user-defined specification. For instance, aJSL script can define expected results for responses for comparing withactual results. Alternatively, or additionally a graphical userinterface can be used to receive a user-defined specification (e.g., togenerate a JSL script). For example, the computing system can evaluatethe one or more resulting responses by indicating a failure indication,in a graphical user interface, for a system under test corresponding toone or more test cases of a design. FIG. 45 , described in more detailherein, shows example failure indications for testing a designedexperiment.

In one or more embodiments, a computing system can identify a mostlikely cause of the failure indication by generating an ordered rankingof a plurality of cause indicators for further testing of the systemunder test and output the failure indication by outputting the orderedranking. For instance, FIG. 46B shows an ordered ranking of one or morepotential causes for further testing (e.g., based on cause indicators).As shown in FIG. 46B and described in more detail herein, the causeindicators can include one or more factors or combinations of thefactors, and the ordered ranking can be based on one or more of theresulting responses, and user-defined weights for the one or morefactors or combinations of the factors. The computing system identifyinga plurality of potential causes for the failure indication for thesystem under test can generate a subsequent design based on theidentified plurality of potential causes, and test the system under testin accordance with the subsequent design.

These tested designs can be generated based on other criteria. Forexample, FIGS. 67A-67B illustrate example graphical user interfaces forspecifying weights (e.g., for coverage criteria 6632 related to maximizeweighted (t+1 coverage)). In FIG. 67A, table 6700 is an editable tableallowing the user to change the default weights of 1. For instance, thefactor X4 has been given a weight of 2. The design then would have moretest cases to determine the influence of X4 on outcomes.

In one or more embodiments, a user can specify certain combinations ofinputs as having greater weight rather than, or in addition to,particular inputs. For example, in FIG. 67B, the user has specified inthe table 6750 the combination 6652 of X1 at L2 and X2 at L1 having aweight of 2, and the combination 6654 of X1 at L1 and X3 at L2 a weightof 3. By weighting these factors or combinations of factors the user cancontrol generation of the design to favor test cases exploring thesefactors or combinations of factors. Users can also explore the criteriaused for generating the designs.

FIGS. 68A-68D illustrate example graphical user interfaces for visualrepresentations of coverage performance. In this example, diagnosticsreflect confounding number metrics for combinations. In the example inFIG. 68A, a table 6800 shows a test suite of 5 test cases generated bythe computing system. The test suite is generated in response to astrength indication indicating a strength of 2 for a covering array withfour binary factors using a selected minimum option for the number oftest cases.

In one or more embodiments, a computing system can determine a set ofconfounding metrics. For instance, the set of confounding metrics cancomprise a confounding metric for each combination of two factors of theinitial design. For instance, FIG. 68B shows a set of confoundingmetrics in table 6830.

In one or more embodiments, the computing system can additionally, oralternatively, provide a visualization of diagnostics. For instance,FIG. 68C provides an example visualization for the strength 2 coveringarray of FIG. 68A and the confounding diagnostics in FIG. 68B. As shownin FIGS. 68B and 68C, the 2-factor combinations may have non-zeroconfounding numbers and pairwise factors. All level combinations areplotted in FIG. 68C. Other diagnostic tools could be used (e.g., designfractal plots can be useful for higher-order combinations of factors asdescribed in more detail with respect to FIG. 70 ). By using thesevisual diagnostics, the user can determine if more test cases should beadded (e.g., to improve the confounding numbers).

In one or more embodiments, a computing system generates an updateddesign (e.g., in response to the user indicating to add test cases) byremoving or adding test cases to an initial design based on theconfounding metrics. For example, the computing system can deriveinformation from the confounding metrics and add or remove test cases toimprove the derived information. For instance, the computing system cancompute a maximum or average of the set of confounding metrics andupdate the design by removing or adding test cases to decrease themaximum or average of the set of confounding metrics.

For instance, in FIG. 68D the user has updated the number of runs toinclude 8 runs (3 above the minimum) and an additional factor X5 toproduce a graphical representation of the updated design 6780. In one ormore embodiments, a graphical representation can be used to visuallydistinguish test cases for the user's consideration. For example, thetest cases 6862 are set apart in this example to show that they areabove the minimum. This can be useful for the test engineer duringtesting. For example, the test engineer can decide during testingwhether to continue with those additional tests (e.g., if the testing istaking longer than normal or the results are as expected). As anotherexample, factor column 6864 is set apart. This could be used todistinguish factors that may not be as important to the design (e.g.,they were down weighted, they are blocking factors, or were added as asecondary consideration).

FIGS. 69A-69B illustrate example flow diagrams for generating an updateddesign. FIG. 69A shows a flow diagram 6900 in which in an operation 6901a computing system receives an indication of a user-defined criteria forthe updated design. For instance, the computing system can receive theuser-defined criteria using a graphical user interface to define input,strength indications, and/or responses (such as failure indications)described herein.

Additionally, or alternatively the user-defined criteria can includerestrictions. For example, restrictions could include disallowedcombinations, or a modification of a disallowed combination as describedherein. Disallowed combinations provide a first set of options that arerestricted from being assigned to a first factor if a second factor ofthe factors is assigned one of a second set of options in the updateddesign. For instance, FIGS. 49A-49C provide example graphical userinterfaces for receiving scripts pertaining to a disallowed combinationor modified disallowed combination. Modified disallowed combinations canbe used, for example, to discretize a range of continuous options givenfor a restricted set of options for a factor. As another example,restrictions could include requirements for test cases or factors in theinitial design. For example, if the design is for an experiment testinga production process for a product, a factor could be required to test aset of materials for producing the product, and certain number of testcases may be required to test a certain material of the set. Theresulting generated design can generate the updated design asconstrained by the restrictions (e.g., the requirements or thedisallowed combination). In an operation 6902 the user specifies aminimum run size or user-defined run size. Run size could also bereferred to as test case size or test size.

If the user specifies a minimum number of test cases, the computingsystem can obtain an initial design. For instance, in an operation 6903the computing system can determine whether there is a directconstruction. If there is a direction construction, the computing systemcan select the theoretical smallest test suite available in an operation6904, if there is not a direct construction, the computing system cangenerate an initial covering array in an operation 6905 and use anoptimizer to find an ideal smallest test suite in an operation 6906(e.g., by using optimizing metrics discussed herein such as ones relatedto diversity, coverage, and confounding factors).

If the user specifies a user-defined run size, the flow chart 6950 inFIG. 69B can be used. In an operation 6951, the computing system canobtain an initial design (e.g., by obtaining an initial covering array).The initial design could be generated or selected in a number of ways.The computing system could obtain the initial design by generating theinitial design that represents a minimum quantity of test cases forcomprising the covering array according to the strength indication.Additionally, or alternatively, the computing system can obtain theinitial design by selecting a pre-configured initial designconstruction. Additionally, or alternatively, the initial design couldbe randomly selected or pre-defined. The initial design could optionallybe further optimized (e.g., optimizing metrics discussed herein such asones related to diversity, coverage, and confounding factors).

After an initial design is obtained, the computing system determines ifthe current run size of the initial design is greater than theuser-specified run size. In other words, the computing system determinesdoes the initial design have too many or too few test cases for arequested design.

If an initial design has too few test cases, the computing system canadd test cases. For instance, in operation 6955 the computing system canidentify do not care cells or rows. Do not care cells or rows are onesthat have test conditions that can be set to any value without changing,or significantly changing, some criteria (e.g., t coverage metric). Inan operation 6956 the computing system can find assignment of these donot care cells or rows that maximizes one or more considered criteria.For instance, tests cases can be added to get to a required number oftest cases. Additionally, test cases may be modified or swapped out tosatisfy other design criteria. In an operation 6957, the computingsystem can return a best test suite according to considered criteria.

If an initial design has too many test cases, in an operation 6954, thecomputing system can use an optimizer to reduce run size to theuser-specified run size. For instance, the computing system can removetest cases from the initial design. Before or after removing test cases,the computing system can perform operations specified with respect tothe situation in which there are too few test cases. For instance, thecomputing system could modify existing test cases to improve performanceon design criteria in view of removing test cases in an operation 6955and return the best test suite according to criterion in an operation6957.

As an example, a computing system could generate or return an updateddesign for a test suite by receiving an indication of a user-definedcriteria for the updated design (e.g., using a graphical user interfacedescribed herein). For instance, the computing system can identify a setof one or more test conditions or test cases that can be changed withoutchanging the proportion of a particular strength for the initial design.The computing system can generate multiple designs according to the setof one or more test conditions or test cases and select the updateddesign from the multiple designs based on the user-defined criteria.

FIGS. 70A-70C illustrate an example pertaining to visual representationsof test case coverage in at least one embodiment of the presenttechnology.

In FIG. 70A, a test suite 7000 is generated according to embodimentsherein to produce a binary design for seven factors with a coveringarray of strength 3. In this binary design, each of factors X1-X7 can beone of two binary options (either a 1 or −1). Output can be recorded inthe Y column 7002. FIG. 70B shows an example diagnostic tool forvisualizing the coverage of the design. In this example, the designfractal plot 7030 is in a grid view investigating factors X1-X4.

The design fractal plot 7030 comprises an intersection betweenperpendicular axes with a first perpendicular axis 7044 corresponding tothe X1 factor and a second perpendicular axis 7042 corresponding to theX2 factor. These perpendicular axes form four quadrants. Each quadrantincludes a small grid pattern 7040 (with its own set of perpendicularaxes pertaining to two more factors X3 and X4 creating four rectanglesin each quadrant). The small grid pattern 7040 is joined to the designfractal plot 7030 such that a vertex of the small grid pattern 7040 isat an endpoint of axis 7042 and/or an endpoint of axis 7044. The same istrue for small grid patterns in each of the other quadrants of thedesign fractal plot 7030. In the grid view shown in FIG. 70B, testconditions for different factors are indicated by the location of arectangle within the design fractal plot 7030 (i.e., up is positive foroptions for the vertical axis and right is positive for options for thehorizontal axis).

In each of the rectangles of design fractal plot 7030, information isdisplayed regarding the test cases. For instance, in FIG. 70B, theidentity labels of the test cases that pertain to a particular rectangleof the design fractal plot 7030 are displayed within a relevantrectangle. For example, rectangle 7036 has identity labels “7” and “8”displayed within rectangle 7036 to indicate test cases 7 and 8 from testsuite 7000. Each of these test cases have the set (X1=−1, X2=−1, X3=+1,X4=−1) which is indicated by the location of rectangle 7036 within thedesign fractal plot 7030. Other information could be provided. Forinstance, a rectangle could instead display a summation of the number oftest cases to give an idea of overall coverage (e.g., rectangle 7036could instead display a “2” indicating 2 test cases tested testconditions where X1=−1, X2=−1, X3=+1, and X4=−1).

In one or more embodiments, displaying information regarding test casesgraphically in a design fractal plot enables a designer of an experimentto consider coverage of various factors. For example, as shown in thedesign fractal plot 7030, there are no test cases plotted in rectangle7032 and rectangle 7034. This may encourage a designer to consideradding test cases that would depict a test case in those design holes.

The fractal grid is a quick way to investigate projections, whether itbe the worst-case, or a projection of interest. In this example, byprojecting down to X1 through X4, a designer can quickly identify wherethe test condition gaps are for those factors and get a sense as to howwell things are covered. In this example in FIG. 70C a designer canmanually add test cases 7060 (e.g., adding test case 25 and test case 26to test suite 7000 in FIG. 70A) to fill in those gaps for the particularprojection. For example, test case 25 has (−1,−1,−1,−1) for X1-X4factors to provide coverage in the rectangle 7032. When test case 25 isadded to test suite 7000, the design fractal plot 7030 could dynamicallyupdate to display the identity label “25” in rectangle 7032. As anotherexample, test case 26 has (+1, −1, −1,+1) for X1-X4 factors to providecoverage in the rectangle 7034. When test case 26 is added to test suite7000, the design fractal plot 7030 could dynamically update to displayidentity label “26” in rectangle 7034.

Other types of design fractal plots (e.g., tree view, plots with more orless than four factors, plots with factors of more than just twooptions, plotting results of testing) are discussed in more detail inU.S. Pat. No. 10,386,271, incorporated herein by reference.

Accordingly, embodiments herein can provide an improved system forcomputer exploration of a design. For example, the computing system canconsider user-defined criteria in generation of a design including oneor more criterion related to coverage in the initial design ofcombinations of the factors, diversity in the initial design, andconfounding of factors in the initial design as described in more detailin previous examples. The computing system can provide further tools forvisualizing the coverage.

What is claimed is:
 1. A computer-program product tangibly embodied in anon-transitory machine-readable storage medium, the computer-programproduct including instructions operable to cause a computing system to:receive, using a graphical user interface: a request for a design of acombinatorial test for a test system, wherein the design comprises aplurality of test cases for testing the test system, each test case ofthe plurality of test cases comprising multiple test conditions fortesting one of factors in the test system, and wherein a test conditionof the multiple test conditions comprises one of a plurality of optionsfor a given factor in the test system; a run indication of a totalquantity of the plurality of test cases for the design; a factorindication of a total quantity of the factors; a strength indication fora covering array, wherein the covering array comprises all combinationsfor any subset of t factors, and wherein t is a numerical value; and auser-defined criteria for generating an updated design; generate theupdated design by: selecting, by the computing system, of one or moretest cases to remove from a first design; or adding, by the computingsystem, one or more computer-generated test cases to the first design;and wherein the first design comprises a set of test cases thatrepresent the covering array according to the strength indication;wherein the set of test cases comprise more or fewer test cases than thetotal quantity of the plurality of test cases; and wherein the updateddesign is constrained to the total quantity of the plurality of testcases as indicated by the run indication; and responsive to receivingthe request for the design of the combinatorial test for the testsystem, output a respective setting for each test condition for at leastone test case of the updated design for testing the test system.
 2. Thecomputer-program product of claim 1, wherein the user-defined criteriacomprise one or more criterion related to: coverage in the first designof combinations of the factors, diversity in the first design, whereindiversity is a ratio of the number of combinations of the factorscovered to a total number of the factors, and confounding of the factorsin the first design.
 3. The computer-program product of claim 1, whereinthe instructions are operable to cause the computing system to: computea proportion of coverage in the first design for combinations of sets ofthe factors, wherein the sets each comprise more factors than requiredby the strength indication; and generate the updated design by one ormore of removing, modifying, or adding a test case to increase theproportion of the coverage of the first design.
 4. The computer-programproduct of claim 1, wherein the instructions are operable to cause thecomputing system to: obtain the first design by generating the firstdesign that represents a minimum quantity of test cases for comprisingthe covering array according to the strength indication; and generatethe updated design by adding test cases to the first design.
 5. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to: obtain the first design byselecting a pre-configured initial design construction; and generate theupdated design by removing test cases to the first design.
 6. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to generate the updated designby: receiving, using the graphical user interface, an indication of auser-defined criteria for the updated design; identifying a set of oneor more test conditions or test cases that can be changed withoutchanging the proportion of a particular strength for the first design;generating a plurality of designs according to the set of one or moretest conditions or test cases; and selecting the updated design from theplurality of designs based on the user-defined criteria.
 7. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to: display, in the graphicaluser interface, one or more metrics indicating a performance of theupdated design; receive, using the graphical user interface, a userindication to change the total quantity of the plurality of test casesfor the requested design; and responsive to the user indication, outputupdated settings for testing the test system, accounting for the changeto the total quantity of the plurality of test cases.
 8. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to display, in the graphical userinterface, one or more metrics indicating a performance of the updateddesign; and wherein the one or more metrics comprise one or more of:coverage in the updated design of combinations of the factors, diversityin the updated design, wherein diversity is a ratio of the number ofcombinations of the factors covered to a total number of the factors,and confounding of factors in the updated design.
 9. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to: submit settings for one ormore test cases of the updated design into the test system; receive auser-defined specification for one or more resulting responsescorresponding to conducting, with the test system, the updated design;and receive an evaluation of the one or more resulting responsesaccording to the user-defined specification.
 10. The computer-programproduct of claim 1, wherein the instructions are operable to cause thecomputing system to: submit settings for one or more test cases of theupdated design into the test system; receive, using the graphical userinterface, one or more resulting responses corresponding to conducting,with the test system, the updated design; and evaluate the one or moreresulting responses by indicating a failure indication, in the graphicaluser interface, for the test system corresponding to a subset of thetest cases of the updated design.
 11. The computer-program product ofclaim 10, wherein the instructions are operable to cause the computingsystem to identify a most likely cause of the failure indication bygenerating an ordered ranking of a plurality of cause indicators forfurther testing of the test system and output the failure indication byoutputting the ordered ranking; wherein the cause indicators compriseone or more factors or combinations of the factors; wherein the orderedranking is based on one or more of: the resulting responses, anduser-defined weights for the one or more factors or combinations of thefactors.
 12. The computer-program product of claim 10, wherein thefactors comprise more than ten factors and wherein the numerical value tis greater than or equal to 3; identify a plurality of potential causesfor the failure indication for the test system; generate a subsequentdesign based on the identified plurality of potential causes; and testthe test system in accordance with the subsequent design.
 13. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to: receive, using the graphicaluser interface, one or more constraints on generating the updateddesign; wherein the one or more constraints comprise one or moredisallowed combinations, or a modification of the one or more disallowedcombinations; wherein the one or more disallowed combinations comprise,for a first factor of the factors, a first set of options that arerestricted from being assigned to the first factor if a second factor ofthe factors is assigned one of a second set of options in the updateddesign; and wherein the first set of options comprise one or more of: arange of continuous options, and discrete options.
 14. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to: receive, using the graphicaluser interface, one or more constraints on generating the first designand updated design; wherein the one or more constraints comprise one ormore requirements for test cases or factors in the first design; andoutput the respective setting for each test condition by displaying inthe graphical user interface the updated design as constrained by theone or more requirements.
 15. The computer-program product of claim 1,wherein the instructions are operable to cause the computing system toreceive, using the graphical user interface, design informationcomprising the run indication, the factor indication, and the strengthindication by one or more of: receiving, using the graphical userinterface, a user request for the requested design and retrieving one ormore preconfigured default values for the design information; andreceiving user input explicitly specifying in the graphical userinterface one or more custom values for the design information.
 16. Thecomputer-program product of claim 1, wherein the instructions areoperable to cause the computing system to: generate a minimum quantityof test cases for the computing system to provide the covering arrayaccording to the strength indication; and display an indication of theminimum quantity of test cases for the computing system to provide thecovering array according to the strength indication in the graphicaluser interface; and wherein the total quantity of the plurality of testcases comprises more test cases than the minimum quantity of test cases.17. The computer-program product of claim 1, wherein the instructionsare operable to cause the computing system to: display in the graphicaluser interface a suggested amount of test cases; and dynamically changethe suggested amount of test cases in response to receiving the factorindication or the strength indication.
 18. A computer-implemented methodcomprising: receiving, using a graphical user interface: a request for adesign of a combinatorial test for a test system, wherein the designcomprises a plurality of test cases for testing the test system, eachtest case of the plurality of test cases comprising multiple testconditions for testing one of factors in the test system, and wherein atest condition of the multiple test conditions comprises one of aplurality of options for a given factor in the test system; a runindication of a total quantity of the plurality of test cases for thedesign; a factor indication of a total quantity of the factors; astrength indication for a covering array, wherein the covering arraycomprises all combinations for any subset of t factors, and wherein t isa numerical value; and a user-defined criteria for generating an updateddesign; generate the updated design by: selecting, by the computingsystem, of one or more test cases to remove from a first design; oradding, by the computing system, one or more computer-generated testcases to the first design; and wherein the first design comprises a setof test cases that represent the covering array according to thestrength indication; wherein the set of test cases comprise more orfewer test cases than the total quantity of the plurality of test cases;and wherein the updated design is constrained to the total quantity ofthe plurality of test cases as indicated by the run indication; andresponsive to receiving the request for the design of the combinatorialtest for the test system, outputting a respective setting for each testcondition for at least one test case of the updated design for testingthe test system.
 19. The computer-implemented method of claim 18,wherein the user-defined criteria comprise one or more criterion relatedto: coverage in the first design of combinations of the factors,diversity in the first design, wherein diversity is a ratio of thenumber of combinations of the factors covered to a total number of thefactors, and confounding of factors in the first design.
 20. Thecomputer-implemented method of claim 18, further comprising computing aproportion of coverage in the first design for combinations of sets ofthe factors, wherein the sets each comprise more factors than requiredby the strength indication; and wherein the generating the updateddesign comprises one or more of removing, modifying, or adding a testcase to increase the proportion of the coverage of the first design. 21.The computer-implemented method of claim 18, further comprisingcomputing a diversity metric for the first design; and wherein thegenerating the updated design comprises removing or adding test case toincrease diversity of the updated design compared to the diversitymetric for the first design.
 22. The computer-implemented method ofclaim 18, further comprising: determining a set of confounding metrics,wherein the set of confounding metrics comprises a confounding metricfor each combination of two factors of the first design; and computing amaximum or average of the set of confounding metrics; and wherein thegenerating the updated design comprises removing or adding test case todecrease the maximum or average of the set of confounding metrics. 23.The computer-implemented method of claim 18, further comprisingobtaining the first design by generating the first design thatrepresents a minimum quantity of test cases for comprising the coveringarray according to the strength indication; and wherein the generatingthe updated design comprises adding test cases to the first design. 24.The computer-implemented method of claim 18, further comprisingobtaining the first design by selecting a pre-configured initial designconstruction; and wherein the generating the updated design comprisesremoving test cases to the first design.
 25. The computer-implementedmethod of claim 18, wherein the generating the updated design comprises:receiving, using the graphical user interface, an indication of auser-defined criteria for the updated design; identifying a set of oneor more test conditions or test cases that can be changed withoutchanging the proportion of a particular strength for the first design;generating a plurality of designs according to the set of one or moretest conditions or test cases; and selecting the updated design from theplurality of designs based on the user-defined criteria.
 26. Thecomputer-implemented method of claim 18, further comprising: displaying,in the graphical user interface, one or more metrics indicating aperformance of the updated design; receiving, using the graphical userinterface, a user indication to change the total quantity of theplurality of test cases for the requested design; and responsive to theuser indication, outputting updated settings for testing the testsystem, accounting for the change to the total quantity of the pluralityof test cases.
 27. The computer-implemented method of claim 18, furthercomprising displaying, in the graphical user interface, one or moremetrics indicating a performance of the updated design; and wherein theone or more metrics comprise one or more of: coverage in the updateddesign of combinations of the factors, diversity in the updated design,wherein diversity is a ratio of the number of combinations of thefactors covered to a total number of the factors, and confounding offactors in the updated design.
 28. A computing device comprisingprocessor and memory, the memory containing instructions executable bythe processor wherein the computing device is configured to: receive,using a graphical user interface: a request for a design of acombinatorial test for a test system, wherein the design comprises aplurality of test cases for testing the test system, each test case ofthe plurality of test cases comprising multiple test conditions fortesting one of factors in the test system, and wherein a test conditionof the multiple test conditions comprises one of a plurality of optionsfor a given factor in the test system; a run indication of a totalquantity of the plurality of test cases for the design; a factorindication of a total quantity of the factors; a strength indication fora covering array, wherein the covering array comprises all combinationsfor any subset of t factors, and wherein t is a numerical value; and auser-defined criteria for generating an updated design; generate theupdated design by: selecting, by the computing system, of one or moretest cases to remove from a first design; or adding, by the computingsystem, one or more computer-generated test cases to the first design;and wherein the first design comprises a set of test cases thatrepresent the covering array according to the strength indication;wherein the set of test cases comprise more or fewer test cases than thetotal quantity of the plurality of test cases; and wherein the updateddesign is constrained to the total quantity of the plurality of testcases as indicated by the run indication; and responsive to receivingthe request for the design of the combinatorial test for the testsystem, output a respective setting for each test condition for at leastone test case of the updated design for testing the test system.
 29. Acomputer-program product tangibly embodied in a non-transitorymachine-readable storage medium, the computer-program product includinginstructions operable to cause a computing system to: receive, using agraphical user interface: a request for a design of a combinatorial testfor a test system, wherein the design comprises a plurality of testcases for testing the test system, each test case of the plurality oftest cases comprising multiple test conditions for testing one offactors in the test system, and wherein a test condition of the multipletest conditions comprises one of a plurality of options for a givenfactor in the test system; a run indication of a total quantity of theplurality of test cases for the design; a factor indication of a totalquantity of the factors; and a strength indication for a covering array,wherein the covering array comprises all combinations for any subset oft factors, and wherein t is a numerical value; compute a diversitymetric for a first design; generate an updated design by: selecting, bythe computing system, of one or more test cases to remove from the firstdesign to increase diversity of the updated design compared to thediversity metric for the first design; or adding, by the computingsystem, one or more computer-generated test cases to the first design toincrease diversity of the updated design compared to the diversitymetric for the first design; and wherein the first design comprises aset of test cases that represent the covering array according to thestrength indication; wherein the set of test cases comprise more orfewer test cases than the total quantity of the plurality of test cases;and wherein the updated design is constrained to the total quantity ofthe plurality of test cases as indicated by the run indication; andresponsive to receiving the request for the design of the combinatorialtest for the test system, output a respective setting for each testcondition for at least one test case of the updated design for testingthe test system.
 30. A computer-program product tangibly embodied in anon-transitory machine-readable storage medium, the computer-programproduct including instructions operable to cause a computing system to:receive, using a graphical user interface: a request for a design of acombinatorial test for a test system, wherein the design comprises aplurality of test cases for testing the test system, each test case ofthe plurality of test cases comprising multiple test conditions fortesting one of factors in the test system, and wherein a test conditionof the multiple test conditions comprises one of a plurality of optionsfor a given factor in the test system; a run indication of a totalquantity of the plurality of test cases for the design; a factorindication of a total quantity of the factors; and a strength indicationfor a covering array, wherein the covering array comprises allcombinations for any subset of t factors, and wherein t is a numericalvalue; determine a set of confounding metrics, wherein the set ofconfounding metrics comprises a confounding metric for multiplecombinations of at least two factors of a first design; compute amaximum or average of the set of confounding metrics; generate anupdated design by: selecting, by the computing system, of one or moretest cases to remove from the first design to decrease the maximum oraverage of the set of confounding metrics; or adding, by the computingsystem, one or more computer-generated test cases to the first design todecrease the maximum or average of the set of confounding metrics; andwherein the first design comprises a set of test cases that representthe covering array according to the strength indication; wherein the setof test cases comprise more or fewer test cases than the total quantityof the plurality of test cases; and wherein the updated design isconstrained to the total quantity of the plurality of test cases asindicated by the run indication; and responsive to receiving the requestfor the design of the combinatorial test for the test system, output arespective setting for each test condition for at least one test case ofthe updated design for testing the test system.